SMT (Statistical Machine Translation) & Dialog Systems 2016


Wikipedia:

References:

See also:

100 Best GitHub: Machine TranslationEBMT (Example-Based Machine Translation) & Dialog Systems | NMT (Neural Machine Translation) & Dialog Systems 2016RBMT (Rule-Based Machine Translation) & Dialog Systems


Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models.
IV Serban, A Sordoni, Y Bengio, AC Courville, J Pineau – AAAI, 2016 – aaai.org
… Therefore, we believe bootstrapping a goal-driven spoken dialogue system based on movie scripts will improve performance … models using word perplex- ity has shown promising results in several machine learn- ing tasks including statistical machine translation (Auli et al …

How NOT to evaluate your dialogue system: An empirical study of unsupervised evaluation metrics for dialogue response generation
CW Liu, R Lowe, IV Serban, M Noseworthy… – arXiv preprint arXiv …, 2016 – arxiv.org
How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation … We investigate evaluation metrics for end- to-end dialogue systems where supervised labels, such as task completion, are not available …

A persona-based neural conversation model
J Li, M Galley, C Brockett, GP Spithourakis… – arXiv preprint arXiv …, 2016 – arxiv.org
… Since generating meaningful re- sponses in an open-domain scenario is intrinsi- cally difficult in conventional dialog systems, ex- isting models often focus on generalizing character style on the basis of qualitative statistical analysis (Walker et al., 2012; Walker et al., 2011) …

Multi-domain neural network language generation for spoken dialogue systems
TH Wen, M Gasic, N Mrksic… – arXiv preprint arXiv …, 2016 – arxiv.org
… Spoken Dialogue Systems … (2015b). These were created by workers recruited by Amazon Mechanical Turk (AMT) by asking them to propose an appropriate natural language realisa- tion corresponding to each system dialogue act ac- tually generated by a dialogue system …

Mutual information and diverse decoding improve neural machine translation
J Li, D Jurafsky – arXiv preprint arXiv:1601.00372, 2016 – arxiv.org
Page 1. Mutual Information and Diverse Decoding Improve Neural Machine Translation Jiwei Li and Dan Jurafsky Computer Science Department Stanford University, Stanford, CA, 94305, USA jiweil,jurafsky@stanford.edu Abstract …

Syntactic filtering and content-based retrieval of twitter sentences for the generation of system utterances in dialogue systems
R Higashinaka, N Kobayashi, T Hirano… – Situated Dialog in …, 2016 – Springer
… and to build social relationships with users [2]. Chat capability also leverages the usability of task-oriented dialogue systems because real … Statistical machine translation techniques have also been utilized to obtain transformation rules (as a phrase table) from input to output …

Deep recurrent models with fast-forward connections for neural machine translation
J Zhou, Y Cao, X Wang, P Li, W Xu – arXiv preprint arXiv:1606.04199, 2016 – arxiv.org
… end fashon. Moreover, NMT models can also be easily adapted to other tasks such as dialog systems (Vinyals and Le, 2015), question answering systems (Yu et al., 2015) and image caption genera- tion (Mao et al., 2015). In …

On-line active reward learning for policy optimisation in spoken dialogue systems
PH Su, M Gasic, N Mrksic, L Rojas-Barahona… – arXiv preprint arXiv …, 2016 – arxiv.org
Page 1. On-line Active Reward Learning for Policy Optimisation in Spoken Dialogue Systems Pei-Hao … learning. 1 Introduction Spoken Dialogue Systems (SDS) allow human- computer interaction using natural speech. They …

Cuni system for wmt16 automatic post-editing and multimodal translation tasks
J Libovický, J Helcl, M Tlustý, P Pecina… – arXiv preprint arXiv …, 2016 – arxiv.org
… In Proceedings of the Second Workshop on Statistical Machine Translation, pages 203–206, Prague, Czech Republic, June. Association for Com- putational Linguistics … 2015. Semantically conditioned lstm-based natural language generation for spoken dialogue systems …

Czeng 1.6: enlarged Czech-English parallel corpus with processing tools dockered
O Bojar, O Dušek, T Kocmi, J Libovický… – … Conference on Text …, 2016 – Springer
… Complete NLP applications such as a dialogue system or a transfer-based MT system are implemented using sequences of processing blocks … P., Monz, C., Peterson, K., Przybocki, M., Zaidan, O.: Findings of the 2010 joint workshop on statistical machine translation and metrics …

A sequence-to-sequence model for user simulation in spoken dialogue systems
LE Asri, J He, K Suleman – arXiv preprint arXiv:1607.00070, 2016 – arxiv.org
… K. Weilhammer, H. Ye, and S. Young, “Agenda-based user simulation for bootstrapping a POMDP dialogue system,” in Proc … F. Bougares, H. Schwenk, and Y. Bengio, “Learning phrase representations using RNN encoder-decoder for statistical machine translation,” CoRR, 2014 …

Automatic construction of discourse corpora for dialogue translation
L Wang, X Zhang, Z Tu, A Way, Q Liu – arXiv preprint arXiv:1605.06770, 2016 – arxiv.org
… (2012) annotated 347 dialogues to explore a spoken dialogue system. The resource of movie scripts, such as IMSDb, is good enough to generate con- versational discourse for dialogue processing … Moses: Open source toolkit for statistical machine translation …

Conditional generation and snapshot learning in neural dialogue systems
TH Wen, M Gasic, N Mrksic… – arXiv preprint arXiv …, 2016 – arxiv.org
Page 1. Conditional Generation and Snapshot Learning in Neural Dialogue Systems … In this work we study various model ar- chitectures and different ways to represent and aggregate the source information in an end- to-end neural dialogue system framework …

Sequence-to-sequence generation for spoken dialogue via deep syntax trees and strings
O Dušek, F Jur?í?ek – arXiv preprint arXiv:1606.05491, 2016 – arxiv.org
… In spoken dialogue systems (SDS), the task of nat- ural language generation (NLG) is to convert a meaning representation (MR) produced by the di- alogue manager into one … Learning Phrase Representations us- ing RNN Encoder-Decoder for Statistical Machine Translation …

Neural Utterance Ranking Model for Conversational Dialogue Systems.
M Inaba, K Takahashi – SIGDIAL Conference, 2016 – aclweb.org
… Proc. ICLR. Rafael E Banchs and Haizhou Li. 2012. Iris: a chat- oriented dialogue system based on the vector space model. In Proceedings of the ACL 2012, pages 37– 42 … 2014. Learning phrase representations using rnn encoder-decoder for statistical machine translation …

Learning distributed representations of sentences from unlabelled data
F Hill, K Cho, A Korhonen – arXiv preprint arXiv:1602.03483, 2016 – arxiv.org
… phrases or sentences as continuous-valued vectors. Examples include machine translation (Sutskever et al., 2014), image captioning (Mao et al., 2015) and dialogue systems (Serban et al., 2015). While it has been ob- served …

Implicit distortion and fertility models for attention-based encoder-decoder NMT model
S Feng, S Liu, M Li, M Zhou – arXiv preprint arXiv:1601.03317, 2016 – arxiv.org
Page 1. Implicit Distortion and Fertility Models for Attention-based Encoder-Decoder NMT Model Shi Feng† Shanghai Jiao Tong University Shanghai, PR China sjtufs@gmail.com Shujie Liu, Mu Li, Ming Zhou Microsoft Research …

Contextual lstm (clstm) models for large scale nlp tasks
S Ghosh, O Vinyals, B Strope, S Roy, T Dean… – arXiv preprint arXiv …, 2016 – arxiv.org
… This has impli- cations for a wide variety of NL applications like question answering, sentence completion, paraphrase generation, and next utterance prediction in dialog systems … This can be used in various applications in dialog systems, eg, intent modeling …

Sequence to backward and forward sequences: A content-introducing approach to generative short-text conversation
L Mou, Y Song, R Yan, G Li, L Zhang, Z Jin – arXiv preprint arXiv …, 2016 – arxiv.org
… (2011) formalize conversation as a statistical machine translation task … systems are generative in that they can synthesize new utterances; results in the literature also show the superiority of seq2seq to phrase-based machine translation for dialogue systems (Shang et al., 2015 …

Learning to respond with deep neural networks for retrieval-based human-computer conversation system
R Yan, Y Song, H Wu – Proceedings of the 39th International ACM SIGIR …, 2016 – dl.acm.org
… Ritter et al. have in- vestigated the feasibility of conducting short text conversation by using statistical machine translation (SMT) techniques, as well as millions of naturally occurring conversation data in Twitter [26]. In the approach …

Knowledge-based semantic embedding for machine translation
C Shi, S Liu, S Ren, S Feng, M Li, M Zhou… – Proceedings of the …, 2016 – aclanthology.info
Page 1. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pages 2245–2254, Berlin, Germany, August 7-12, 2016. cO2016 Association for Computational Linguistics Knowledge-Based Semantic Embedding for Machine Translation …

Syntax-based statistical machine translation
P Williams, R Sennrich, M Post… – Synthesis Lectures on …, 2016 – morganclaypool.com
… v Spoken Dialogue Systems Kristiina Jokinen and Michael McTear 2009 … Syntax-based Statistical Machine Translation Philip Williams, Rico Sennrich, Matt Post, and Philipp Koehn www.morganclaypool.com ISBN: 9781627059008 paperback ISBN: 9781627055024 ebook …

A Simple, Fast Diverse Decoding Algorithm for Neural Generation
J Li, W Monroe, D Jurafsky – arXiv preprint arXiv:1611.08562, 2016 – arxiv.org
Page 1. A Simple, Fast Diverse Decoding Algorithm for Neural Generation Jiwei Li, Will Monroe and Dan Jurafsky Computer Science Department, Stanford University, Stanford, CA, USA jiweil,wmonroe4,jurafsky@stanford.edu Abstract …

A unified framework for translation and understanding allowing discriminative joint decoding for multilingual speech semantic interpretation
B Jabaian, F Lefèvre, L Besacier – Computer Speech & Language, 2016 – Elsevier
… information between them. Keywords. Multilingual speech understanding; Conditional random fields; Hypothesis graphs; Statistical machine translation; Dialogue systems. 1. Introduction. Nowadays, probabilistic approaches …

GuessWhat?! Visual object discovery through multi-modal dialogue
H de Vries, F Strub, S Chandar, O Pietquin… – arXiv preprint arXiv …, 2016 – arxiv.org
… Abstract We introduce GuessWhat?!, a two-player guessing game as a testbed for research on the interplay of computer vision and dialogue systems … Although goal-directed dialogue systems are appeal- ing, they remain hard to design …

Sequence-level knowledge distillation
Y Kim, AM Rush – arXiv preprint arXiv:1606.07947, 2016 – arxiv.org
… in this setting. This approach is inspired by local updating (Liang et al., 2006), a method for discriminative train- ing in statistical machine translation (although to our knowledge not for knowledge distillation). Lo- cal updating …

The aNALoGuE Challenge: Non Aligned Language GEneration.
J Novikova, V Rieser – INLG, 2016 – anthology.aclweb.org
… Statistical Machine Translation, pages 256–273, Lis- bon, Portugal, September. Association for Computa- tional Linguistics. Amanda Stent, Rashmi Prasad, and Marilyn Walker. 2004. Trainable sentence planning for complex infor- mation presentation in spoken dialog systems …

Summarizing Source Code using a Neural Attention Model.
S Iyer, I Konstas, A Cheung, L Zettlemoyer – ACL (1), 2016 – aclweb.org
… Perhaps most closely related, Wen et al. (2015) generate text for spoken dialogue systems with a two-stage approach, comprising an LSTM decoder seman- tically conditioned on the logical representation of speech acts, and a reranker to generate the fi- nal output …

LSTM based Conversation Models
Y Luan, Y Ji, M Ostendorf – arXiv preprint arXiv:1603.09457, 2016 – arxiv.org
… Rit- ter et al. [8] present a statistical machine translation based con- versation system … The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems. arXiv preprint arXiv:1506.08909, 2015 …

Controlling output length in neural encoder-decoders
Y Kikuchi, G Neubig, R Sasano, H Takamura… – arXiv preprint arXiv …, 2016 – arxiv.org
Page 1. Controlling Output Length in Neural Encoder-Decoders Yuta Kikuchi1 kikuchi@lr.pi.titech. ac.jp Graham Neubig2? gneubig@cs.cmu.edu Ryohei Sasano1 sasano@pi.titech.ac.jp Hiroya Takamura1 takamura@pi.titech.ac.jp Manabu Okumura1 oku@pi.titecjh.ac.jp …

Incorporating Loose-Structured Knowledge into LSTM with Recall Gate for Conversation Modeling
Z Xu, B Liu, B Wang, C Sun, X Wang – arXiv preprint arXiv:1605.05110, 2016 – arxiv.org
… As mentioned in Section 1, our motivation of con- versation modeling is to provide reliable evidence to detect users’ context-aware queries and further select best answers based on the conversation his- tory, for building automatic dialog systems …

Neural text generation from structured data with application to the biography domain
R Lebret, D Grangier, M Auli – arXiv preprint arXiv:1603.07771, 2016 – arxiv.org
… This mechanism is inspired by recent work on attention based word copying for neural machine translation (Luong et al., 2015) as well as delexicalization for neural dialog systems (Wen et al., 2015). It also builds upon older …

Improving neural language models with a continuous cache
E Grave, A Joulin, N Usunier – arXiv preprint arXiv:1612.04426, 2016 – arxiv.org
… Peter F Brown, Vincent J Della Pietra, Stephen A Della Pietra, and Robert L Mercer. The mathematics of statistical machine translation: Parameter estimation … Evaluating prerequisite qualities for learning end-to-end dialog systems. arXiv preprint arXiv:1511.06931, 2015 …

Multilingual Spoken Language Understanding using graphs and multiple translations
M Calvo, LF Hurtado, F Garcia, E Sanchis… – Computer Speech & …, 2016 – Elsevier
… If we use Statistical Machine Translation (SMT) systems, such as MOSES (Koehn et al., 2007), it is necessary to have a parallel corpus in … to combine different translations and on determining how to process them in the semantic module of a multilingual Spoken Dialog System …

Dynamic Neural Turing Machine with Soft and Hard Addressing Schemes
C Gulcehre, S Chandar, K Cho, Y Bengio – arXiv preprint arXiv …, 2016 – arxiv.org
Page 1. Dynamic Neural Turing Machine with Soft and Hard Addressing Schemes Caglar Gulcehre?, Sarath Chandar?, Kyunghyun Cho†, Yoshua Bengio? ? University of Montreal, name.lastname@umontreal.ca † New York University, name.lastname@nyu.edu Abstract …

Automatic Corpus Extension for Data-driven Natural Language Generation.
E Manishina, B Jabaian, S Huet, F Lefèvre – LREC, 2016 – lrec-conf.org
… Marton, Y., Callison-Burch, C., and Resnik, P. (2009). Improved statistical machine translation using monolingually-derived paraphrases … Mitchell, M., Redmond, W., Bohus, D., and Kamar, E. (2014). Crowdsourcing language generation templates for dialogue systems …

Dialogue session segmentation by embedding-enhanced texttiling
Y Song, L Mou, R Yan, L Yi, Z Zhu, X Hu… – arXiv preprint arXiv …, 2016 – arxiv.org
… 2.1. Dialogue Systems and Context Modeling Human-computer dialogue systems can be roughly divided into several categories … corpus given a user-issued utterance as a query [8]. Genera- tive methods can synthesize new replies by statistical machine translation [16, 17] or …

Squeezing bottlenecks: exploring the limits of autoencoder semantic representation capabilities
P Gupta, RE Banchs, P Rosso – Neurocomputing, 2016 – Elsevier
We present a comprehensive study on the use of autoencoders for modelling text data, in which (differently from previous studies) we focus our attention on the.

Chinese poetry generation with planning based neural network
Z Wang, W He, H Wu, H Wu, W Li, H Wang… – arXiv preprint arXiv …, 2016 – arxiv.org
… 2012. Generating chinese classical poems with statistical machine translation models … 2016. How not to evaluate your dialogue system: An empirical study of unsupervised evaluation metrics for dialogue response generation. arXiv preprint arXiv:1603.08023 …

DocChat: An Information Retrieval Approach for Chatbot Engines Using Unstructured Documents.
Z Yan, N Duan, JW Bao, P Chen, M Zhou, Z Li, J Zhou – ACL (1), 2016 – aclweb.org
… vSj represent the word vector of jth word in S and vQj repre- sent the word vector of ith word in Q. 4.2 Phrase-level Feature 4.2.1 Paraphrase We first describe how to extract phrase-level para- phrases from an existing SMT (statistical machine translation) phrase table …

An attentional neural conversation model with improved specificity
K Yao, B Peng, G Zweig, KF Wong – arXiv preprint arXiv:1606.01292, 2016 – arxiv.org
Page 1. An Attentional Neural Conversation Model with Improved Specificity Kaisheng Yao Microsoft Research Redmond, USA kaisheny@microsoft.com Baolin Peng Chinese University of Hong Kong blpeng@se.cuhk.edu.hk …

Seeing is believing: the quest for multimodal knowledge by Gerard de Melo and Niket Tandon, with Martin Vesely as coordinator
G de Melo, N Tandon – ACM SIGWEB Newsletter, 2016 – dl.acm.org
… In speech recognition, markedly lower error rates have enabled powerful dialog systems, including Siri on Apple’s iOS, Alexa for Amazon Echo, and … Then a statistical machine translation engine or a recurrent neural network with LSTM or GRU units is used to predict an output …

A Corpus and Semantic Parser for Multilingual Natural Language Querying of OpenStreetMap.
C Haas, S Riezler – HLT-NAACL, 2016 – anthology.aclweb.org
… We use the corpus to learn an ac- curate semantic parser that builds the basis of a natural language interface to OSM. Fur- thermore, we use response-based learning on parser feedback to adapt a statistical machine translation system for multilingual database access to OSM …

Listen, Attend, and Walk: Neural Mapping of Navigational Instructions to Action Sequences.
H Mei, M Bansal, MR Walter – AAAI, 2016 – aaai.org
Page 1. Listen, Attend, and Walk: Neural Mapping of Navigational Instructions to Action Sequences Hongyuan Mei Mohit Bansal Matthew R. Walter Toyota Technological Institute at Chicago Chicago, IL 60637 {hongyuan,mbansal,mwalter}@ttic.edu Abstract …

Category-driven content selection
R Mohammed, L Perez-Beltrachini… – Proceedings of the 9th …, 2016 – aclweb.org
… 2008. Making grammar-based generation easier to deploy in dialogue systems. In Proceedings of the 9th SIGdial Workshop on Discourse and Dialogue, pages 198–207 … 2007. Genera- tion by inverting a semantic parser that uses statistical machine translation …

Recurrent Neural Networks for Dialogue State Tracking
O Plátek, P B?lohlávek, V Hude?ek… – arXiv preprint arXiv …, 2016 – arxiv.org
… Informal experiments were conducted during the Statistical Dialogue Systems course at Charles University (see https://github.com/oplatek/sds-tracker). Page 5 … Learning Phrase Representations us- ing RNN Encoder-Decoder for Statistical Machine Translation …

Statistical Natural Language Generation from Tabular Non-textual Data.
J Mahapatra, SK Naskar, S Bandyopadhyay – INLG, 2016 – anthology.aclweb.org
… Philipp Koehn. 2010. Statistical Machine Translation. Cambridge University Press, New York, NY, USA, 1st edition … 2009. Mountain: a translation-based approach to natural language gener- ation for dialog systems. Kathleen McKeown, Karen Kukich, and James Shaw. 1994 …

Improving Attention Modeling with Implicit Distortion and Fertility for Machine Translation.
S Feng, S Liu, N Yang, M Li, M Zhou, KQ Zhu – COLING, 2016 – aclweb.org
… Philipp Koehn. 2004. Pharaoh: a beam search decoder for phrase-based statistical machine translation models … 2015. Se- mantically conditioned lstm-based natural language generation for spoken dialogue systems. arXiv preprint arXiv:1508.01745 …

Controlling the voice of a sentence in japanese-to-english neural machine translation
H Yamagishi, S Kanouchi, T Sato… – Proceedings of the 3rd …, 2016 – anthology.aclweb.org
… However, compared to traditional statistical machine translation, it is not straightforward to interpret and control the output of the encoder-decoder models … For example, one may prefer a polite expression for generating conversation in a dialog system …

RACAI Entry for the IWSLT 2016 Shared Task
S Pipa, AF Vasile, I Ionascu… – Proceedings of the …, 2016 – workshop2016.iwslt.org
… extremely important for automatic machine translation (MT), speech-to-speech translation, information extraction, dialog systems, etc … Schwenk, H., &Bengio, Y. (2014).Learning phrase representations using RNN encoder-decoder for statistical machine translation.arXiv preprint …

JUNITMZ at SemEval-2016 Task 1: Identifying Semantic Similarity Using Levenshtein Ratio.
S Sarkar, D Das, P Pakray, AF Gelbukh – SemEval@ NAACL-HLT, 2016 – aclweb.org
… Methods for measuring the textual sim- ilarity are useful to a broad range of applica- tions including: text mining, information re- trieval, dialogue systems, machine translation and text summarization … In Proceedings of the EACL 2014 Workshop on Statistical Machine Translation …

Log-linear rnns: Towards recurrent neural networks with flexible prior knowledge
M Dymetman, C Xiao – arXiv preprint arXiv:1607.02467, 2016 – arxiv.org
Page 1. Log-Linear RNNs : Towards Recurrent Neural Networks with Flexible Prior Knowledge (Version 1.0) Marc Dymetman Chunyang Xiao Xerox Research Centre Europe, Grenoble, France {marc.dymetman,chunyang.xiao}@xrce.xerox.com Monday 11th July, 2016 Abstract …

Recent Advances on Human-Computer Dialogue
X Wang, C Yuan – CAAI Transactions on Intelligence Technology, 2016 – Elsevier
… Abstract. Human-Computer dialogue systems provide a natural language based interface between human and computers. They are widely demanded in network information services, intelligent accompanying robots, and so on … 2. Frames of goal-driven dialogue systems. Fig …

Sample-efficient Deep Reinforcement Learning for Dialog Control
K Asadi, JD Williams – arXiv preprint arXiv:1612.06000, 2016 – arxiv.org
… In future work we will apply the method to a dialog system with real human users Page 5. References … Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078, 2014. Franois Chollet. Keras. https://github …

Investigating critical speech recognition errors in spoken short messages
A Pappu, T Misu, R Gupta – Situated Dialog in Speech-Based Human …, 2016 – Springer
… In this work, we focus on analyzing, distinguishing these critical errors from non-critical errors and finally detecting them in the text messages domain. In information-access dialog systems, slot-value words are more relevant than others …

Natural Language Generation through Character-based RNNs with Finite-state Prior Knowledge.
R Goyal, M Dymetman, E Gaussier, U LIG – COLING, 2016 – aclweb.org
… An RNN-based approach to NLG has been recently proposed by Wen et al. (2015) in the context of a dialog system, where the input semantic representation is a Dialog Act (DA) … Learning phrase representations using rnn encoder-decoder for statistical machine translation …

A semantic analyzer for the comprehension of the spontaneous Arabic speech
M Mars, M Zrigui, M Belgacem, A Zouaghi – arXiv preprint arXiv …, 2016 – arxiv.org
… des jeunes chercheurs en génie électrique et informatique, Mahdia, Tunisie (2003) 8. Macherey, K., Och, FJ, Ney, H.: Natural Language Understanding Using Statistical Machine Translation … Workshop on Spoken Dialogue Systems, Vigso, Danemark (1995) 65-68 12 …

A step beyond local observations with a dialog aware bidirectional GRU network for Spoken Language Understanding
V Vukotic, C Raymond, G Gravier – Interspeech, 2016 – hal.inria.fr
… MEDIA The research project MEDIA [13] evaluates different SLU mod- els of spoken dialogue systems dedicated to provide tourist in … H. Schwenk, and Y. Bengio, “Learning phrase rep- resentations using RNN encoder-decoder for statistical machine translation,” arXiv preprint …

Assisting discussion forum users using deep recurrent neural networks
JHP Suorra, O Mogren – Proceedings of the 1st Workshop on …, 2016 – aclweb.org
… generation from questions as input, and training the model using two posts as input and the following response as target.(Serban et al., 2016) presented a dialog system built as a … Learning phrase representations using rnn encoder-decoder for statistical machine translation …

Learning to Start for Sequence to Sequence Architecture
Q Zhu, W Zhang, L Zhou, T Liu – arXiv preprint arXiv:1608.05554, 2016 – arxiv.org
… (2016), who came up with the Hierarchical Nerual Network model, aiming to model the utterances and interactive structure to build a multi-round dialogue system … Learning phrase representations using rnn encoder-decoder for statistical machine translation. Computer Science …

Generating Paraphrases from DBPedia using Deep Learning
A Sleimi, C Gardent – WebNLG 2016, 2016 – webnlg2016.sciencesconf.org
… 2014. Fast and robust neural network joint models for statistical machine translation. In ACL (1), pages 1370–1380. Citeseer … 2015. Semantically conditioned ltsm-base natural lan- guage generation for spoken dialogue systems …

Learning to Query, Reason, and Answer Questions On Ambiguous Texts
X Guo, T Klinger, C Rosenbaum, JP Bigus, M Campbell… – 2016 – openreview.net
… questions. There has also been significant recent interest in learning task-oriented dialog systems such as by Bordes & Weston (2016); Dodge et al. (2016); Williams & Zweig (2016); Henderson et al. (2014); Young et al. (2013) …

Dynamic Neural Turing Machine with Continuous and Discrete Addressing Schemes
C Gulcehre, S Chandar, K Cho, Y Bengio – 2016 – openreview.net
Page 1. Under review as a conference paper at ICLR 2017 DYNAMIC NEURAL TURING MACHINE WITH CONTIN- UOUS AND DISCRETE ADDRESSING SCHEMES Caglar Gulcehre?, Sarath Chandar?, Kyunghyun Cho†, Yoshua …

Accurate semantic similarity measurement of biomedical nomenclature by means of fuzzy logic
J Martinez-Gil – International Journal of Uncertainty, Fuzziness and …, 2016 – World Scientific
… 5. TW Bickmore and T. Giorgino, Health dialog systems for patients and consumers, Journal of Biomedical Informatics 39 (2006) 556–571 … 37 (2013) 62–69. 8. B. Chen, GF Foster and R. Kuhn, Bilingual sense similarity for statistical machine translation, ACL 2010, pp. 834–843 …

RNN-based Encoder-decoder Approach with Word Frequency Estimation
J Suzuki, M Nagata – arXiv preprint arXiv:1701.00138, 2016 – arxiv.org
… [2014], Cho et al. [2014] question answering Xu et al. [2015], dialogue system Vinyals and Le [2015], Shang et al. [2015], and abstractive summarization (ABS) Rush et al … Learning Phrase Representations us- ing RNN Encoder–Decoder for Statistical Machine Translation …

Discovering User Attribute Stylistic Differences via Paraphrasing.
D Preotiuc-Pietro, W Xu, LH Ungar – AAAI, 2016 – aaai.org
… style. We use the phrase-based statistical machine translation decoder Moses (Koehn et al … Linguistic individuality transformation for spoken language. In Natural Language Dialog Systems and Intelligent Assistants, 129–143. Newman …

Deterministic stack transducers
S Bensch, J Björklund, M Kutrib – International Conference on …, 2016 – Springer
… We find them in, for example, speech processing [12], machine translation [3], and increasingly in dialog systems [9]. A disadvantage of the … 3. Braune, F., Seemann, N., Quernheim, D., Maletti, A.: Shallow local multi-bottom-up tree transducers in statistical machine translation …

Performance improvement of Machine Translation system using LID and post-editing
K Mrinalini, G Sangavi… – Region 10 Conference …, 2016 – ieeexplore.ieee.org
… The performance of the statistical machine translation system needs to be tweaked to higher levels and then improved by post-editing or … The translation system can be extended to a dialogue system where the translation occurs between speech from one language to another [5 …

Improving Statistical Machine Translation with Target-Side Dependency Syntax
WR John – 2016 – repository.kulib.kyoto-u.ac.jp
Page 1. Title Improving Statistical Machine Translation with Target-Side Dependency Syntax( Dissertation_?? ) Author(s) John, Walter Richardson Citation Kyoto University (????) … Page 2. Improving Statistical Machine Translation with Target-Side Dependency Syntax …

TEXT NORMALIZATION FOR AUTOMATIC SPEECH RECOGNITION SYSTEMS
AF VASILE, T BORO? – Editors: Maria Mitrofan Daniela Gîfu Dan Tufi? … – consilr.info.uaic.ro
… Bang, J., Park, S., Lee, GG (2015). ASR Independent Hybrid Recurrent Neural Network Based Error Correction for Dialog System Applications. In … Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv: 1406.1078 …

Sequence Generation & Dialogue Evaluation
R Lowe – cs.mcgill.ca
… phrase representations using RNN encoder-decoder for statistical machine translation.” EMNLP, 2014. Kingma & Welling. “Auto-encoding Variational Bayes.” ICLR, 2014. Liu, Lowe, Serban, Noseworthy, Charlin, Pineau. “How NOT to Evaluate Your Dialogue System: A Study of …

Proceedings of the 2nd Workshop on Semantics-Driven Machine Translation (SedMT 2016)
D Xiong, K Duh, E Agirre, N Aranberri… – Proceedings of the 2nd …, 2016 – aclweb.org
… and machine translation, as well as a thought-provoking panel discussion on the gaps and challenges between semantics and statistical machine translation … Since then, he held post-doc positions at the University of Edinburgh, work- ing on spoken dialogue systems, and the …

Data-driven natural language generation using statistical machine translation and discriminative learning
E Manishina – 2016 – theses.fr
… communicate with us in our language. Most modern systems communicating directly with the user share one common feature: they have a dialog system (DS) at their base. As of … 30 2.2 Statistical Machine Translation . . . . . 33 …

An English-Arabic Real Time System (EARS)
AA Sakr – International Journal – pdfs.semanticscholar.org
… It trains the Viterbi training that takes the most likely solution. Viterbi training is much faster than Baum-Welch approach[13]. Viterbi is applied in dialogue systems, where desired semantic output is more clear … In Proc. of the 3rd Workshop on Statistical Machine Translation, pp …

Machine translation of spoken English into Czech
O Cífka – dspace.cuni.cz
… This equation captures the noisy channel model, which is the basis of statistical machine translation … Our test recordings were transcribed according to guidelines used in dialogue systems (see Sections 1.2 and 2.2.2 for details) and directly translated into Czech to obtain …

Domain adaptation of a speech translation system for lectures by utilizing frequently appearing parallel phrases in-domain
N Goto, K Yamamoto… – Signal and Information …, 2016 – ieeexplore.ieee.org
… Lectures tend to have much broader topics than such speech translation tasks as spoken dialog systems for travel assistance [2]. The … consists of an ASR based on DNN-HMM which is additionally trained by in- domain lectures and statistical machine translation (SMT) based on …

McGill Reasoning & Learning Lab: Research Overview
R Lowe – cs.mcgill.ca
… “Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation.” EMNLP 2014. Serban, Sordoni, Bengio, Courville, Pineau. “Building End-to-End Dialogue Systems using Generative Hierarchical Neural Network Models” AAAI, 2015 …

Deep Learning for Natural Language Processing-Research at Noah’s Ark Lab
H Li – 2016 – hangli-hl.com
… Machine Translation • Natural Language Dialogue Page 20. Statistical Machine Translation and Neural Machine Translation NMT: RNN Search … Alan Turing Page 27. Natural Language Dialogue System – Retrieval based Approach index of messages and responses …

Toward Designing a Realistic Conversational System: A Survey.
AM Ali, AJ Gonzalez – FLAIRS Conference, 2016 – aaai.org
… (2013) use statistical machine translation and example-based … Banchs and Li (2012) introduced IRIS (Informal Re- sponse Interactive System), a chat-oriented dialogue system that learns new concepts from users, and semantically relates them to its previous knowledge …

A Dialog Act Tagger for Telugu
D Suman – 2016 – web2py.iiit.ac.in
… Her vast knowledge in Computational Linguistics especially in Dialog Systems helped me to build two strong papers which in-turn contributed in completing my Masters … 1 1.1 Dialog System . . . . . 1 …

A multichannel convolutional neural network for cross-language dialog state tracking
H Shi, T Ushio, M Endo, K Yamagami… – … Workshop (SLT), 2016 …, 2016 – ieeexplore.ieee.org
… neural networks for multi-topic dialog state tracking,” Pro- ceedings of the 7th International Workshop on Spoken Dialogue Systems (IWSDS), 2016 … [11] David Vilar, Jia Xu, Luis Fernando dHaro, and Hermann Ney, “Error analysis of statistical machine translation output,” in …

A Train-on-Target Strategy for Multilingual Spoken Language Understanding
F García-Granada, E Segarra, C Millán… – Advances in Speech …, 2016 – Springer
… One of the areas of application of SLU systems is Spoken Dialogue Systems for limited domains … If we use Statistical Machine Translation (SMT) systems, such as MOSES [11], it is necessary to have a parallel corpus in both languages that must be specifically designed for the …

Backchanneling via Twitter Data for Conversational Dialogue Systems
M Inaba, K Takahashi – International Conference on Speech and …, 2016 – Springer
… Other studies based on the statistical machine translation method include [14, 17]. 5 Conclusions. In this study, we proposed a method for generating a rich variety of backchanneling for conversational dialogue systems to realize smooth human–machine communication …

Automatic Correction of ASR outputs by Using Machine Translation
LF D’Haro, RE Banchs – 2016 – pdfs.semanticscholar.org
… Which ASR should I choose for my dialogue system? Proc. SIGDIAL, August … 3, pp. 203-212). [11] Brown, PF, Pietra, VJD, Pietra, SAD, & Mercer, RL (1993). The mathematics of statistical machine translation: Parameter estimation. Computational linguistics, 19(2), 263-311 …

Translation (SedMT 2016)
D Xiong, K Duh, E Agirre, N Aranberri, H Wang – 2016 – anthology.aclweb.org
… and machine translation, as well as a thought-provoking panel discussion on the gaps and challenges between semantics and statistical machine translation … Since then, he held post-doc positions at the University of Edinburgh, work- ing on spoken dialogue systems, and the …

Non-sentential Question Resolution using Sequence to Sequence Learning.
V Kumar, S Joshi – COLING, 2016 – aclweb.org
… 2014. Learning phrase representations using rnn encoder-decoder for statistical machine translation. In EMNLP … 2016. Building end- to-end dialogue systems using generative hierarchical neural network models. In AAAI. Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014 …

A Train-on-Target Strategy for Multilingual Spoken Language Understanding
E Sanchis, LF Hurtado – Advances in Speech and Language …, 2016 – books.google.com
… One of the areas of application of SLU systems is Spoken Dialogue Systems for limited domains … If we use Statistical Machine Translation (SMT) systems, such as MOSES [11], it is necessary to have a parallel corpus in both languages that must be specifically designed for the …

USTC at NTCIR-12 STC Task.
J Zhang, J Hou, S Zhang, LR Dai – NTCIR, 2016 – pdfs.semanticscholar.org
… To build a traditional dialogue system which contains several components[1], a lot of related technologies have been de- veloped such as dialogue state tracking[2], natural language … Learning phrase representations using rnn encoder-decoder for statistical machine translation …

Stylistic Transfer in Natural Language Generation Systems Using Recurrent Neural Networks
J Kabbara, JCK Cheung – EMNLP 2016, 2016 – aclweb.org
… Bengio. 2014b. Learning phrase rep- resentations using rnn encoder–decoder for statistical machine translation. In … Young. 2015. Seman- tically conditioned lstm-based natural language gener- ation for spoken dialogue systems. In …

IRT-based Aggregation Model of Crowdsourced Pairwise Comparison for Evaluating Machine Translations.
N Otani, T Nakazawa, D Kawahara, S Kurohashi – EMNLP, 2016 – anthology.aclweb.org
… We conducted experi- ments on a public dataset from the Workshop on Statistical Machine Translation 2013, and found that our approach resulted … It is also essential for natural lan- guage processing tasks such as summarization and dialogue systems, where (1) the number of …

Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations
H Watanabe – Proceedings of COLING 2016, the 26th International …, 2016 – aclweb.org
… 11 A Post-editing Interface for Immediate Adaptation in Statistical Machine Translation Patrick Simianer, Sariya Karimova and Stefan Riezler … 273 Zara: A Virtual Interactive Dialogue System Incorporating Emotion, Sentiment and Personality Recog- nition Pascale Fung, Anik Dey …

UT Dialogue System at NTCIR-12 STC.
S Sato, S Ishiwatari, N Yoshinaga, M Toyoda… – …, 2016 – pdfs.semanticscholar.org
… 3. EXPERIMENTS This section evaluates our domain-aware dialogue system on the response retrieval task … [6] H. Yamamoto and E. Sumita. Bilingual cluster based models for statistical machine translation. In Proceedings of EMNLP-CoNLL, pages 514–523, 2007 …

A survey of voice translation methodologies—Acoustic dialect decoder
H Krupakar, K Rajvel, B Bharathi… – Information …, 2016 – ieeexplore.ieee.org
… Statistical Machine Translation is one where the source sentence is encoded into a representation which is translated into target language by maximising the probability of the closeness of the target sentence by using Bayes rule … [16] P. Koehn, Statistical Machine Translation …

Shall I Be Your Chat Companion?: Towards an Online Human-Computer Conversation System
R Yan, Y Song, X Zhou, H Wu – … of the 25th ACM International on …, 2016 – dl.acm.org
… have investigated the feasibility of conducting short text conversation by using statistical machine translation (SMT) techniques, as well as millions of naturally occurring conversation data in Twitter [26] … Statistical Machine Translation …

Detecting Context Dependent Messages in a Conversational Environment
C Li, Y Wu, W Wu, C Xing, Z Li, M Zhou – arXiv preprint arXiv:1611.00483, 2016 – arxiv.org
… Differing from traditional dialogue systems (cf., (Young et al., 2013)) which rely on hand-crafted features and rules to generate reply sentences for specific applications such as voice … Learning phrase representations using rnn encoder-decoder for statistical machine translation …

Deep Recurrent Models with Fast-Forward Connections for Neural Machine Translation
JZYCX Wang, PLW Xu – pdfs.semanticscholar.org
… form. Moreover, NMT models can also be easily adapted to other tasks such as dialog systems (Vinyals and Le, 2015), question answering systems (Yu et al., 2015) and image caption genera- tion (Mao et al., 2014). Generally …

Multilingual Multimodal Language Processing Using Neural Networks
MM Khapra, S Chandar – Proceedings of the 2016 Conference of the …, 2016 – aclweb.org
… His areas of interest include Statistical Machine Translation, Text Analytics, Crowdsourcing, Argument Mining and Deep Learning … dialog systems. His research interests includes Machine Learning, Natural Language Processing, Deep Learning, and Reinforcement Learning …

Addressee and Response Selection for Multi-Party Conversation.
H Ouchi, Y Tsuboi – EMNLP, 2016 – 2boy.org
… tic rules or templates (Levin et al., 2000; Young et al., 2010; Walker et al., 2003), they apply statistical machine translation based techniques … Basically, the addressee detection has been tackled in the spoken/multimodal dialog system research, and the models largely rely on …

Neural Paraphrase Generation with Stacked Residual LSTM Networks
A Prakash, SA Hasan, K Lee, V Datla, A Qadir… – arXiv preprint arXiv …, 2016 – arxiv.org
… 2015), speech recognition (Li and Wu, 2015), language modeling (Vinyals et al., 2015), and dialogue systems (Serban et … generation have applied relatively different methodologies, typically us- ing knowledge-driven approaches or statistical machine translation (SMT) principles …

Which techniques does your application use?: An information extraction framework for scientific articles
S Dan, S Agarwal, M Singh, P Goyal… – arXiv preprint arXiv …, 2016 – arxiv.org
… For example, paper title, “Moses: Open source toolkit for statistical machine translation” [13] represents an instance of the form … entity recognition, word alignment, conditional random fields, maximum entropy, corefer- ence resolution, machine learning, dialogue systems, tex- tual …

Neural Network Approaches to Dialog Response Retrieval and Generation
L Nio, S Sakti, G Neubig, K Yoshino… – … on Information and …, 2016 – search.ieice.org
… generates unnatural re- sponses that are incomprehensible to the user [9]. There have been a number of works on response gener- ation for data-driven dialog systems. The first work in data- driven response generation utilized a statistical machine translation system to …

Kannada speech to text conversion using CMU Sphinx
KM Shivakumar, KG Aravind… – Inventive …, 2016 – ieeexplore.ieee.org
… Page 2. to have a human computer dialogue system in any local language … Statistical machine translation approach was used in this process; the noisy channel adds noise to its input sentence for disfluencies and creates the sentence in the source language as output …

Producing Monolingual and Parallel Web Corpora at the Same Time-SpiderLing and Bitextor’s Love Affair.
N Ljubesic, M Esplà-Gomis, A Toral, S Ortiz-Rojas… – LREC, 2016 – lrec-conf.org
… Do- main adaptation of statistical machine translation with domain-focused web crawling. Language Resources and Evaluation, pages 1–47 … Com- paring intrinsic and extrinsic evaluation of MT output in a dialogue system. In IWSLT, pages 329–336 …

Globally Coherent Text Generation with Neural Checklist Models
C Kiddon, L Zettlemoyer, Y Choi – … of the 2016 Conference on Empirical …, 2016 – aclweb.org
… Evaluations on cooking recipes and dialogue system responses demonstrate high coherence with greatly improved semantic coverage of the agenda. 1 Introduction … (2016) present neural network models for generating dialogue system responses given a set of agenda items …

Response Selection with Topic Clues for Retrieval-based Chatbots
Y Wu, W Wu, Z Li, M Zhou – arXiv preprint arXiv:1605.00090, 2016 – arxiv.org
… Although previous research on conversation focused on dialog systems, recently, with the large amount of con- versation data available … 2015; Lu and Li 2013), and generation based methods employ statistical machine translation techniques (Ritter, Cherry, and Dolan 2011) or …

A neural network approach for knowledge-driven response generation
P Vougiouklis, J Hare, E Simperl – 2016 – eprints.soton.ac.uk
… 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. CoRR, abs/1406.1078 … In Proceedings of the 2010 Workshop on Companionable Dialogue Systems, CDS ’10, pages 43–48, Stroudsburg, PA, USA …

Report on the Eighth Workshop on Exploiting Semantic Annotations in Information Retrieval (ESAIR’15)
K Balog, J Dalton, A Doucet, Y Ibrahim – ACM SIGIR Forum, 2016 – dl.acm.org
… The authors propose to compensate for this lack of resources by leveraging resources available in English, and exploiting them using Statistical Machine Translation (SMT) to translate English re- sources into the target language … Question answering and dialog systems …

Automatic Diacritics Restoration for Dialectal Arabic Text
AA Zayyan, M Elmahdy, H binti Husni… – International Journal of …, 2016 – ijcis.info
… This approach is based on statistical-machine translation … From 2007 to 2011, he was pursuing his Ph.D. degree at the Dialogue Systems Group, Institute of Information Technology at the University of Ulm in cooperation with the German University in Cairo …

Category-Driven Content Selection
RM Sayed, L Perez-Beltrachini… – The 9th International …, 2016 – anthology.aclweb.org
… 2008. Making grammar-based generation easier to deploy in dialogue systems. In Proceedings of the 9th SIGdial Workshop on Discourse and Dialogue, pages 198–207 … 2007. Genera- tion by inverting a semantic parser that uses statistical machine translation …

Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
K Knight, A Nenkova, O Rambow – … of the 2016 Conference of the North …, 2016 – aclweb.org
… 110 xxi Page 22. Multi-domain Neural Network Language Generation for Spoken Dialogue Systems Tsung-Hsien Wen, Milica Gašic, Nikola Mrkšic, Lina M. Rojas-Barahona, Pei-Hao Su, David Vandyke and Steve Young …

Military Usages of Speech and Language Technologies: A Review.
D Griol, JG Herrero, JM Molina – Meeting Security Challenges …, 2016 – books.google.com
… access remote information. For this reason, spoken dialog systems (SDS)[1, 2, 3] are becoming a strong alternative to traditional graphical interfaces, which might not be appropriate for all users and/or applications. These systems …

Spoken Language Understanding
M McTear, Z Callejas, D Griol – The Conversational Interface, 2016 – Springer
… In the examples above, there were utterances about setting alarms, finding restaurants, getting information about spoken dialog systems, and asking about personal feelings … Dialog state tracking involves estimating a user’s goal in a spoken dialog system …

Assisting Discussion Forum Users using Deep Recurrent Neural Networks
J Suorra Hagstedt P, O Mogren – Proceedings of the …, 2016 – publications.lib.chalmers.se
… (Serban et al., 2016) presented a dialog system built as a hierarchical … 2014. Learning phrase representations using rnn encoder-decoder for statistical machine translation. In Alessandro Moschitti, Bo Pang, and Walter Daelemans, editors, EMNLP, pages 1724–1734. ACL …

Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
K Erk, NA Smith – Proceedings of the 54th Annual Meeting of the …, 2016 – aclanthology.info
Page 1. The 54th Annual Meeting of the Association for Computational Linguistics Proceedings of the Conference, Vol. 1 (Long Papers) August 7-12, 2016 Berlin, Germany Page 2. Platinum Sponsors Gold Sponsors Silver Sponsors ii Page 3. Bronze Sponsors …

Cognitive Human–Robot Interaction
B Mutlu, N Roy, S Šabanovi? – Springer Handbook of Robotics, 2016 – Springer
… Robotics 22, 1343–1359 (2008)CrossRef. [71.114]. C. Matuszek, D. Fox, K. Koscher: Following directions using statistical machine translation, Proc … 229–232. [71.141]. J. Peltason, B. Wrede: The curious robot as a case-study for comparing dialog systems, AI Magazine 32(4), 85 …

CoTO: A novel approach for fuzzy aggregation of semantic similarity measures
J Martinez-Gil – Cognitive Systems Research, 2016 – Elsevier
… Past works in this field include the automatic processing of text messages (Lamontagne & Lapalme, 2004), healthcare dialogue systems (Bickmore & Giorgino, 2006), natural language querying of databases (Erozel, Cicekli, & Cicekli, 2008) and question answering (Moschitti & …

A generalized framework for anaphora resolution in Indian languages
UK Sikdar, A Ekbal, S Saha – Knowledge-Based Systems, 2016 – Elsevier
In this paper, we propose a joint model of feature selection and ensemble learning for anaphora resolution in the resource-poor environment like the Indian lang.

Collaborative Review in Writing Analytics: N-Gram Analysis of Instructor and Student Comments.
A Rudniy, N Elliot – EDM (Workshops), 2016 – pdfs.semanticscholar.org
… n-gram applications in speech recognition, optical character recognition, spelling correction, handwriting recognition, and statistical machine translation … Forbes-Riley and Litman [40] have developed approaches for adapting student affect in intelligent tutoring dialogue systems …

Sentence Level Recurrent Topic Model: Letting Topics Speak for Themselves
F Tian, B Gao, D He, TY Liu – arXiv preprint arXiv:1604.02038, 2016 – arxiv.org
… [Cho et al.,2014] Kyunghyun Cho, Bart V Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. Learning phrase representations using rnn encoder–decoder for statistical machine translation …

Review of state-of-the-arts in artificial intelligence. Present and future of AI.
V Shakirov – alpha.sinp.msu.ru
… ”Statistical Machine Translation Features with Multitask Tensor Networks” http: //arxiv.org/abs/1506.00698 … ”Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models” http:// arxiv.org/abs/1507.04808 …

Personified Autoresponder
A Mahendra – cs224d.stanford.edu
… [Cho+14] Kyunghyun Cho et al. “Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation”. In: EMNLP … [Ser+16] Iulian Vlad Serban et al. “Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models” …

PersoNER: Persian Named-Entity Recognition
H Poostchi, E Zare Borzeshi, M Abdous… – The 26th International …, 2016 – opus.lib.uts.edu.au
… Applications Tim Baldwin Maria Liakata Dialog Processing and Dialog Systems, Multimodal Interfaces Nina Dethlefs Simon Keizer Giuseppe Riccardi Speech Recognition, Text-To-Speech, Spoken Language Understanding Florian Metze Chung-Hsien Wu …

Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
Y Matsumoto, R Prasad – Proceedings of COLING 2016, the 26th …, 2016 – aclweb.org
… Applications Tim Baldwin Maria Liakata Dialog Processing and Dialog Systems, Multimodal Interfaces Nina Dethlefs Simon Keizer Giuseppe Riccardi Speech Recognition, Text-To-Speech, Spoken Language Understanding Florian Metze Chung-Hsien Wu …

Context Based Morphological Analysis
MD Kumar – 2016 – web2py.iiit.ac.in
… Radhika Mamidi for guiding me in an independent project which gave me the scope to explore more about Dialog Systems … [41] presents an unsupervised learning approach to building a Arabic stemmer which is based on statistical machine translation and parallel corpora as …

A natural language interface concordant with a knowledge base
YJ Han, SB Park, SY Park – Computational intelligence and …, 2016 – dl.acm.org
… producing an incorrect answer. In order to overcome this limita- tion, the classification-based approach was proposed. The classification-based approach has been extensively studied for dialog systems [7, 19]. It trains classifiers for …

Compressing neural language models by sparse word representations
Y Chen, L Mou, Y Xu, G Li, Z Jin – arXiv preprint arXiv:1610.03950, 2016 – arxiv.org
… they can model language more precisely than traditional n-gram statistics (Mikolov et al., 2011); it is even possible to gen- erate new sentences from a neural LM, benefit- ing various downstream tasks like machine trans- lation, summarization, and dialogue systems (De- vlin et …

Towards an Entertaining Natural Language Generation System: Linguistic Peculiarities of Japanese Fictional Characters.
C Miyazaki, T Hirano, R Higashinaka, Y Matsuo – SIGDIAL Conference, 2016 – aclweb.org
… the fictional characters’ lin- guistic peculiarities and (ii) reveal the impact of each category on characterizing dialogue system utterances … that transforms individual characteristics in dia- logue agent utterances using a method based on statistical machine translation (Mizukami et …

A learned representation for artistic style
V Dumoulin, J Shlens, M Kudlur, A Behboodi… – arXiv preprint arXiv …, 2016 – arxiv.org
… Comments: 34 pages, 3 figures. Subjects: Probability (math.PR); Computational Complexity (cs.CC). arXiv:1610.08000 [pdf, other] Title: Statistical Machine Translation for Indian Languages: Mission Hindi 2 …

A multi-agent conversational system with heterogeneous data sources access
EM Eisman, M Navarro, JL Castro – Expert Systems with Applications, 2016 – Elsevier
… interactive NLIs: used in dialog systems, they have memory to remember previous questions … The whole learning process was done using statistical machine translation techniques with minimal supervision, so it was not necessary to manually develop a grammar in different …

Statistical analysis of multivariate data in bioinformatics
T Metsalu – 2016 – dspace.ut.ee
Page 1. I DISSERTATIONES MATHEMATICAE UNIVERSITATIS TARTUENSIS 103 TAUNO METSALU Statistical analysis of multivariate data in bioinformatics Page 2. DISSERTATIONES MATHEMATICAE UNIVERSITATIS TARTUENSIS 103 Page 3 …

Low-Resource Active Learning of Morphological Segmentation
SA Grönroos, K Hiovain, P Smit, I Rauhala, K Jokinen… – 2016 – research.aalto.fi
Page 1. This is an electronic reprint of the original article. This reprint may differ from the original in pagination and typographic detail. Powered by TCPDF (www.tcpdf.org) This material is protected by copyright and other intellectual …

Review of state-of-the-arts in artificial intelligence with application to AI safety problem
V Shakirov – arXiv preprint arXiv:1605.04232, 2016 – arxiv.org
… ”Statistical Machine Translation Features with Multitask Tensor Networks” http: //arxiv.org/abs/1506.00698 … ”Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models” http:// arxiv.org/abs/1507.04808 …

Notational Conventions and Symbols
KI Baldwin, S Ishizaki, H Nakagawa, A Shimazu – people.eng.unimelb.edu.au
Page 1. Bond, Francis and Timothy Baldwin (2016) Introduction to Japanese Computational Linguistics, In Francis Bond, Timothy Baldwin, Kentaro Inui, Shun Ishizaki, Hiroshi Nakagawa and Akira Shimazu (eds.) Readings in …

Sequential Match Network: A New Architecture for Multi-turn Response Selection in Retrieval-based Chatbots
Y Wu, W Wu, M Zhou, Z Li – arXiv preprint arXiv:1612.01627, 2016 – arxiv.org
… an index based on matching be- tween the response and an input message with or without context (Hu et al., 2014; Ji et al., 2014; Wang et al., 2015; Yan et al., 2016; Wu et al., 2016; Zhou et al., 2016), while the latter employs statistical machine translation techniques (Ritter et al …

An Exploratory Study on Process Representations
CN Naik – 2016 – search.proquest.com
… have many potential applications in NLP and have been shown to benet question answering [7, 8], textual entailment [9], machine translation [1012], and dialogue systems [13, 14 … Corpus expansion for statistical machine translation with semantic role label substitution rules …

Prototypical Recurrent Unit
D Long, R Zhang, Y Mao – arXiv preprint arXiv:1611.06530, 2016 – arxiv.org
Page 1. arXiv:1611.06530v1 [cs.LG] 20 Nov 2016 Prototypical Recurrent Unit Dingkun Long1, Richong Zhang1, Yongyi Mao2 1School of Computer Science and Engineering, Beihang University, Beijing,China 2School of Electrical …

Speaky for robots: the development of vocal interfaces for robotic applications
E Bastianelli, D Nardi, LC Aiello, F Giacomelli… – Applied …, 2016 – Springer

The strategic impact of META-NET on the regional, national and international level
G Rehm, H Uszkoreit, S Ananiadou, N Bel… – Language resources …, 2016 – Springer
… This includes all types of information and communication technologies such as, for example, general and domain-specific machine translation systems, dialogue systems, automatic subtitling, tourist information systems etc …

Semantic language models with deep neural networks
AO Bayer, G Riccardi – Computer Speech & Language, 2016 – Elsevier
… language models (LMs) are one of the main knowledge sources in language processing systems such as statistical machine translation, information retrieval … This may lead to problems especially for spoken dialog systems, where one of the main goals of these systems is to …

N-gram Approximation of Latent Words Language Models for Domain Robust Automatic Speech Recognition
R Masumura, T Asami, T Oba, H Masataki… – … on Information and …, 2016 – search.ieice.org
Page 1. 2462 IEICE TRANS. INF. & SYST., VOL.E99–D, NO.10 OCTOBER 2016 PAPER Special Section on Recent Advances in Machine Learning for Spoken Language Processing N-gram Approximation of Latent Words Language Models for …

A data-driven approach to automatic tweet generation about traffic incidents
MK Tran – 2016 – summit.sfu.ca
… Some applications belonging to this category are text summarization, dialogue systems and automatic question generation. The MATCH dialogue system [12, 45] is an NLG system that gives users restaurant rec- ommendations …

Natural vibrations of elastic stepped arches with cracks
A Liyvapuu – 2016 – dspace.ut.ee
Page 1. I DISSERTATIONES MATHEMATICAE UNIVERSITATIS TARTUENSIS 106 A L E X A N D E RL IY V A P U U Natural vibrations of elastic stepped arches with cracks ALEXANDER LIYVAPUU Natural vibrations of elastic stepped arches with cracks Page 2 …

Improving Social Inclusion Using NLP: Tools and Resources
I Schuurman, V Vandeghinste, H Saggion – 2016 – lrec-conf.org
Page 1. LREC 2016 Workshop Improving Social Inclusion Using NLP: Tools and Resources PROCEEDINGS Edited by Ineke Schuurman, Vincent Vandeghinste, Horacio Saggion 23 May 2016 Page 2. Proceedings of the LREC …

SUBTLE: Situation Understanding Bot through Language and Environment
HA Yanco, H Kress-Gazit, H Yanco, DJ Brooks… – 2016 – dtic.mil
Page 1. Standard Form 298 (Rev 8/98) Prescribed by ANSI Std. Z39.18 215-898-2538 W911NF-07-1-0216 52555-MA-MUR.19 Final Report a. REPORT 14. ABSTRACT 16. SECURITY CLASSIFICATION OF: While highly constrained …

Linguistic Knowledge in Data-Driven Natural Language Processing
Y Tsvetkov – 2016 – cs.cmu.edu
… 20 2.3 Models of Lexical Borrowing in Statistical Machine Translation … for example, to translate African languages, to detect metaphors in Russian and Persian, to grammatically parse Cantonese, to model Latin or Hebrew morphology, to build dialog systems for indigenous …

Designing Regularizers and Architectures for Recurrent Neural Networks
D Krueger – 2016 – papyrus.bib.umontreal.ca
Page 1. Université de Montréal Designing Regularizers and Architectures for Recurrent Neural Networks par David Krueger Département d’informatique et de recherche opérationnelle Faculté des arts et des sciences Mémoire …

Incremental Segmentation and Annotation Strategies for Real-time Natural Language Processing Applications
M Yarmohammadi – 2016 – digitalcommons.ohsu.edu
… 33 2.6 Statistical Machine Translation … translation. 1.3 Organization of the Dissertation In the next chapter, we provide an overview of the technical preliminaries of finite- state methods, context-free methods, parsing, and statistical machine translation. We …

Index of the Ph. D Dissertation: Confidence Measures for Automatic and Interactive Speech Recognition
IS Cortina – 2016 – riunet.upv.es
Page 1. i i i i Confidence Measures for Automatic and Interactive Speech Recognition ? January, 2016 ? Ph.D. Dissertation by Isaías Sánchez Cortina Advisors: Dr. Alfons Juan i Ciscar Dr. J. Alberto Sanchis Navarro Page 2. Page 3. Resum …

School of Computing
R Cruise – 2016 – vlebb.leeds.ac.uk
Page 1. CO-DISPERSION BY NEAREST-NEIGHBOUR: ADAPTING A SPATIAL STATISTIC FOR THE DEVELOPMENT OF DOMAIN-INDEPENDENT LANGUAGE TOOLS AND METRICS Justin Washtell MSc Multidisciplinary Informatics 2006/07 …

Language modeling for automatic speech recognition of inflective languages: an applications-oriented approach using lexical data
G Donaj, Z Ka?i? – 2016 – books.google.com
… The development of speech technologies enables a more natural way of interaction with computers systems. Several technologies are needed for such an interaction: speech recognition, dialog systems and speech synthesis …

Multi-behavioral Sequential Prediction for Collaborative Filtering
Q Liu, S Wu, L Wang – arXiv preprint arXiv:1608.07102, 2016 – pdfs.semanticscholar.org
Page 1. Multi-behavioral Sequential Prediction for Collaborative Filtering Qiang Liu, Shu Wu, Liang Wang Center for Research on Intelligent Perception and Computing (CRIPAC) National Laboratory of Pattern Recognition (NLPR …

Linked lexical knowledge bases: Foundations and applications
I Gurevych, J Eckle-Kohler… – Synthesis Lectures on …, 2016 – morganclaypool.com
… Syntax-based Statistical Machine Translation Philip Williams, Rico Sennrich, Matt Post, and Philipp Koehn 2016 Bayesian Analysis in Natural Language Processing Shay Cohen 2016 … Spoken Dialogue Systems Kristiina Jokinen and Michael McTear 2009 Page 7. v …

Bayesian analysis in natural language processing
S Cohen – Synthesis Lectures on Human Language …, 2016 – morganclaypool.com
… Bayesian Analysis in Natural Language Processing Shay Cohen 2016 Syntax-based Statistical Machine Translation Philip Williams, Rico Sennrich, Matt Post, and Philipp Koehn 2016 … Spoken Dialogue Systems Kristiina Jokinen and Michael McTear 2009 …

Sequential decisions and predictions in natural language processing
H He – 2016 – search.proquest.com
Sequential decisions and predictions in natural language processing. Abstract. Natural language processing has achieved great success in a wide range of applications, producing both commercial language services and open-source language tools …

Grounding robot motion in natural language and visual perception
SA Bronikowski – 2016 – search.proquest.com
… Instructions [7] . . . . 82.2 Toward Understanding Natural Language Directions [8] . . . . 122.3 Following Directions Using Statistical Machine Translation [9] . . . . 152.4 Understanding Natural Language Commands for Robotic Navigation and …

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