NMT (Neural Machine Translation) & Dialog Systems 2016


Wikipedia:

References:

See also:

100 Best GitHub: Machine Translation | EBMT (Example-Based Machine Translation) & Dialog Systems | RBMT (Rule-Based Machine Translation) & Dialog Systems | SMT (Statistical Machine Translation) & Dialog Systems 2016


A network-based end-to-end trainable task-oriented dialogue system
TH Wen, D Vandyke, N Mrksic, M Gasic… – arXiv preprint arXiv …, 2016 – arxiv.org
… [1] D. Bahdanau, K. Cho, and Y. Bengio. Neural machine translation by jointly learning to align and translate. ICLR, 2015 … On-line policy optimisation of bayesian spoken dialogue systems via human interaction. In ICASSP, 2013. [4] M. Henderson, B. Thomson, and S. Young …

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) …

Deep reinforcement learning for dialogue generation
J Li, W Monroe, A Ritter, M Galley, J Gao… – arXiv preprint arXiv …, 2016 – arxiv.org
… To achieve these goals, we draw on the insights of reinforcement learning, which have been widely ap- plied in MDP and POMDP dialogue systems (see Re- lated Work section for details) … 2 Related Work Efforts to build statistical dialog systems fall into two major categories …

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
… Neural machine translation (NMT) has attracted a lot of interest in solving the machine translation (MT) problem in recent years (Kalchbrenner and … Moreover, NMT models can also be easily adapted to other tasks such as dialog systems (Vinyals and Le, 2015), question …

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 … To avoid this enormous search space, we propose to use a reranking approach to approximate the mutual information between source and target in neural machine translation models …

Video paragraph captioning using hierarchical recurrent neural networks
H Yu, J Wang, Z Huang, Y Yang… – Proceedings of the IEEE …, 2016 – cv-foundation.org
… To our knowledge, this is the first application of hierarchical RNN to video captioning task. 2. Related Work Neural Machine Translation. The methods for NMT [18, 9, 1, 43, 27, 28] in computational linguistics generally fol- low the encoder-decoder paradigm …

Sequence-to-sequence learning as beam-search optimization
S Wiseman, AM Rush – arXiv preprint arXiv:1606.02960, 2016 – arxiv.org
… and training have also proven to be useful for sen- tence compression (Filippova et al., 2015), parsing (Vinyals et al., 2015), and dialogue systems (Ser- ban … This model architecture has been found to be highly performant for neural machine translation and other seq2seq tasks …

Sequential short-text classification with recurrent and convolutional neural networks
JY Lee, F Dernoncourt – arXiv preprint arXiv:1603.03827, 2016 – arxiv.org
… 2014. On the properties of neural machine translation: Encoder- decoder approaches. arXiv preprint arXiv:1409.1259 … Adobe- MIT submission to the DSTC 4 Spoken Language Un- derstanding pilot task. In 7th International Workshop on Spoken Dialogue Systems (IWSDS) …

Conditional generation and snapshot learning in neural dialogue systems
TH Wen, M Gasic, N Mrksic… – arXiv preprint arXiv …, 2016 – arxiv.org
… This paper has investigated different conditional generation architectures and a novel method called snapshot learning to improve response generation in a neural dialogue system framework … 2015. Neural machine translation by jointly learning to align and translate. In ICLR …

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
… 2016. Coverage-based neural machine translation. CoRR, abs/1601.04811. [Turchi et al.2016] Marco Turchi, Rajen Chatterjee, and Matteo Negri. 2016 … 2015. Semantically conditioned lstm-based natural language generation for spoken dialogue systems …

A neural knowledge language model
S Ahn, H Choi, T Pärnamaa, Y Bengio – arXiv preprint arXiv:1608.00318, 2016 – arxiv.org
… understanding. Beyond its usage as a standalone application, it has been an indispensable component in many language/speech tasks such as speech recognition [26, 1], machine translation [17], and dialogue systems [40, 34] …

Neural Utterance Ranking Model for Conversational Dialogue Systems.
M Inaba, K Takahashi – SIGDIAL Conference, 2016 – aclweb.org
… 2015. Neural machine translation by jointly learning to align and translate. 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 …

NewsQA: A Machine Comprehension Dataset
A Trischler, T Wang, X Yuan, J Harris, A Sordoni… – arXiv preprint arXiv …, 2016 – arxiv.org
… Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. Neural machine translation by jointly learning to align and translate. ICLR, 2015 … How not to evaluate your dialogue system: An empirical study of unsupervised evaluation metrics for dialogue response generation …

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
… com Abstract Neural machine translation has shown very promising results lately. Most NMT models follow the encoder-decoder frame- work … 1 Introduction Neural machine translation has shown promising results lately. Most …

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
… We thank Jiexiu Zhai, Hao Meng, Siqi Wang, Zhexi Hong, Junling Chen, and Zhiliang Tian for eval- uating our dialogue systems. We also thank all reviewers for their constructive comments … 2014. On the properties of neural machine translation: Encoder-decoder approaches …

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 or more sentences in a natural … Neural Machine Translation by Jointly Learning to Align and Translate …

Hierarchical memory networks
S Chandar, S Ahn, H Larochelle, P Vincent… – arXiv preprint arXiv …, 2016 – arxiv.org
… Evaluating prerequisite qualities for learning end-to-end dialog systems. CoRR, abs/1511.06931, 2015 … On using very large target vocabulary for neural machine translation. In Proceedings of ACL,2015, pages 1–10, 2015. [22] Ryan Spring and Anshumali Shrivastava …

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 …

A Simple, Fast Diverse Decoding Algorithm for Neural Generation
J Li, W Monroe, D Jurafsky – arXiv preprint arXiv:1611.08562, 2016 – arxiv.org
… For reranking, we first generate a large N-best list using the beam search algorithm.16 We then rerank the hypotheses using features that have been shown to be useful in neural machine translation (Sennrich et al., 2015a; Gulcehre et al., 2015; Cohn et al., 2016; Cheng et al …

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 …

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 …

Sequence-level knowledge distillation
Y Kim, AM Rush – arXiv preprint arXiv:1606.07947, 2016 – arxiv.org
… Abstract Neural machine translation (NMT) offers a novel alternative formulation of translation that is potentially simpler than statistical ap- proaches … In this work, we investigate knowledge distilla- tion in the context of neural machine translation …

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
… generate it. 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 …

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 …

End-to-End Memory Networks with Knowledge Carryover for Multi-Turn Spoken Language Understanding.
YN Chen, D Hakkani-Tür, G Tür, J Gao… – …, 2016 – pdfs.semanticscholar.org
… In the past decades, goal-oriented spoken dialogue systems (SDS) are being incorporated in various devices and allow users to speak to systems … [29] K. Cho, B. van Merriënboer, D. Bahdanau, and Y. Bengio, “On the properties of neural machine translation: Encoder-decoder …

Improving neural language models with a continuous cache
E Grave, A Joulin, N Usunier – arXiv preprint arXiv:1612.04426, 2016 – arxiv.org
… Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473, 2014 … Evaluating prerequisite qualities for learning end-to-end dialog systems. arXiv preprint arXiv:1511.06931, 2015 …

Sequence-based Structured Prediction for Semantic Parsing.
C Xiao, M Dymetman, C Gardent – ACL (1), 2016 – aclweb.org
Page 1. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pages 1341–1350, Berlin, Germany, August 7-12, 2016. cO2016 Association for Computational Linguistics Sequence-based Structured Prediction for Semantic Parsing …

Overview of NTCIR-13
MP Kato, Y Liu, C Gurrin, H Joho… – Proceedings of the …, 2016 – research.nii.ac.jp
… we propose the Associative Conversation Model that generates visual information from textual information and uses it for generating sentences in order to utilize visual information in a dialogue system without image input. In research on Neural Machine Translation, there are …

Character-Level Neural Translation for Multilingual Media Monitoring in the SUMMA Project
G Barzdins, S Renals, D Gosko – arXiv preprint arXiv:1604.01221, 2016 – arxiv.org
… processing part of the SUMMA project (the dark block in Fig.1), where the recently developed neural machine translation techniques (Sutskerev … generalization and memorization capacity of the neural networks, which is already applied in the neural dialogue systems such as …

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
… 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 … 2016. How not to evaluate your dialogue system: An empirical study of unsupervised evaluation metrics for dialogue response generation …

Question detection from acoustic features using recurrent neural network with gated recurrent unit
Y Tang, Y Huang, Z Wu, H Meng… – Acoustics, Speech and …, 2016 – ieeexplore.ieee.org
… Second, in most spoken dialog systems, automatic speech recognition (ASR) is the foremost step whose performance will have huge impacts on the … [12] KH Cho, BV Merriënboer, D. Bahdanau, Y. Bengio, “On the properties of neural machine translation: Encoder- decoder …

An attentional neural conversation model with improved specificity
K Yao, B Peng, G Zweig, KF Wong – arXiv preprint arXiv:1606.01292, 2016 – arxiv.org
… corpus. Neural conversation models are usually trained similarly to neural machine translation mod- els (Sutskever et al., 2014; Cho et al., 2014), which treat response generation as a surface-to-surface transformation. While …

Natural language generation in dialogue using lexicalized and delexicalized data
S Sharma, J He, K Suleman, H Schulz… – arXiv preprint arXiv …, 2016 – arxiv.org
… However, Liu et al. (2016) showed that the BLEU metric does not correlate well with human judgement for domain- specific NLG in dialogue systems … “Neural Machine Translation by Jointly Learning to Align and Translate”. In: CoRR abs/1409.0473 …

Dialog state tracking with attention-based sequence-to-sequence learning
T Hori, H Wang, C Hori, S Watanabe… – … (SLT), 2016 IEEE, 2016 – ieeexplore.ieee.org
… 1. INTRODUCTION Recently, spoken dialog systems have been widely used for many human-machine interfaces such as smart phones and car navigation systems. Spoken language understanding (SLU) technology, which …

Task Lineages: Dialog State Tracking for Flexible Interaction.
S Lee, A Stent – SIGDIAL Conference, 2016 – aclweb.org
… study the potential impact on other dialog system compo- nents of providing more comprehensive state rep- resentations to SLU and action selection. 18 Page 37. References Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly …

Online Sequence-to-Sequence Reinforcement Learning for Open-Domain Conversational Agents
N Asghar, P Poupart, J Xin, H Li – arXiv preprint arXiv:1612.03929, 2016 – arxiv.org
… Neural machine translation by jointly learning to align and translate. In ICLR, 2015. [Cuayáhuitl et al.,2016] Heriberto Cuayáhuitl, Seunghak Yu, Ashley Williamson, and Jacob Carse. Deep reinforce- ment learning for multi-domain dialogue systems …

Generating text from structured data with application to the biography domain
R Lebret, D Grangier, M Auli – ArXiv e-prints, March, 2016 – pdfs.semanticscholar.org
Page 1. Generating Text from Structured Data with Application to the Biography Domain Remi Lebret IDIAP Research Institute Martigny, Switzerland remi@lebret.ch David Grangier Facebook AI Research Menlo Park, California grangier@fb.com …

Context-aware Natural Language Generation with Recurrent Neural Networks
J Tang, Y Yang, S Carton, M Zhang, Q Mei – arXiv preprint arXiv …, 2016 – arxiv.org
… There are usually two types of approaches for natural lan- guage generation: beam search (Bahdanau, Cho, and Bengio 2014), which is widely used in neural machine translation, and random sample (Graves … Stochastic lan- guage generation for spoken dialogue systems …

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
… attempt to add a ability of voice control to an encoder-decoder model, based on Sen- nrich et al.(2016), which controls the honorifics in English-German neural machine translation … For example, one may prefer a polite expression for generating conversation in a dialog system …

A context-aware natural language generator for dialogue systems
O Dušek, F Jur?í?ek – arXiv preprint arXiv:1608.07076, 2016 – arxiv.org
… eloquence and fluency, and most importantly, eval- uating our generator in a live dialogue system … D. Bahdanau, K. Cho, and Y. Bengio. 2015. Neural machine translation by jointly learning to align and translate. In International Conference on Learning Representations …

Encoder-decoder with Focus-mechanism for Sequence Labelling Based Spoken Language Understanding
S Zhu, K Yu – arXiv preprint arXiv:1608.02097, 2016 – arxiv.org
… In spoken dialogue system, the Spoken Language Under- standing (SLU) is a key component that parses user utter- ances into corresponding semantic concepts … [16] Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Ben- gio, “Neural machine translation by jointly learning to …

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 …

Recurrent Neural Networks for Dialogue State Tracking
O Plátek, P B?lohlávek, V Hude?ek… – arXiv preprint arXiv …, 2016 – arxiv.org
… com/oplatek/e2end/ under Apache license. Informal experiments were conducted during the Statistical Dialogue Systems course at Charles University (see https://github.com/oplatek/sds- tracker). Page 5 … Neural machine translation by jointly learning to align and translate …

Two are Better than One: An Ensemble of Retrieval-and Generation-Based Dialog Systems
Y Song, R Yan, X Li, D Zhao, M Zhang – arXiv preprint arXiv:1610.07149, 2016 – arxiv.org
… as the encoder and decoder, such architecture is also known as a seq2seq model, which has wide applications in neural machine translation [21], abstractive summarization [15], etc. That being said, previous studies indicate seq2seq has its own shortcoming for dialog systems …

Reference-Aware Language Models
Z Yang, P Blunsom, C Dyer, W Ling – arXiv preprint arXiv:1611.01628, 2016 – arxiv.org
… We first apply our model on task-oriented dialogue systems in the domain of restaurant recommenda- tions, and work on the data set from the second Dialogue State Tracking Challenge (DSTC2) (Hen- derson et al., 2014) … Table 2: Fragment of database for dialogue system …

Syntax or semantics? knowledge-guided joint semantic frame parsing
YN Chen, D Hakanni-Tür, G Tur… – … (SLT), 2016 IEEE, 2016 – ieeexplore.ieee.org
… tur , +asli }@ieee . org , {+jfgao , +deng}@microsoft.com ABSTRACT Spoken language understanding (SLU) is a core component of a spo- ken dialogue system, which involves intent prediction and slot filling and also called semantic frame parsing …

Context-aware Natural Language Generation for Spoken Dialogue Systems.
H Zhou, M Huang, X Zhu – COLING, 2016 – aclweb.org
… To evaluate the performance of CA-LSTM, we adopt the SF Restaurant dataset as used in (Wen et al., 2015), which is a corpus of a spoken dialogue system providing information about restaurants in San … 2014. Neural machine translation by jointly learning to align and translate …

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
… 2016. Coverage-based neural machine translation. arXiv preprint arXiv:1601.04811 … 2015. Se- mantically conditioned lstm-based natural language generation for spoken dialogue systems. arXiv preprint arXiv:1508.01745. Huijuan Xu and Kate Saenko. 2015 …

Length bias in encoder decoder models and a case for global conditioning
P Sountsov, S Sarawagi – arXiv preprint arXiv:1606.03402, 2016 – arxiv.org
Page 1. Length bias in Encoder Decoder Models and a Case for Global Conditioning Pavel Sountsov Google siege@google.com Sunita Sarawagi ? IIT Bombay sunita@iitb.ac.in Abstract Encoder-decoder networks are popular …

Optimizing neural network hyperparameters with gaussian processes for dialog act classification
F Dernoncourt, JY Lee – Spoken Language Technology …, 2016 – ieeexplore.ieee.org
Page 1. OPTIMIZING NEURAL NETWORK HYPERPARAMETERS WITH GAUSSIAN PROCESSES FOR DIALOG ACT CLASSIFICATION Franck Dernoncourt*, Ji Young Lee * MITCSAIL Cambridge, MA, USA {francky,jjylee}@mit.edu ABSTRACT …

Learning to Start for Sequence to Sequence Architecture
Q Zhu, W Zhang, L Zhou, T Liu – arXiv preprint arXiv:1608.05554, 2016 – arxiv.org
… Using the sequence to sequence model, neural machine translation has already got a comparable per- formance to the traditional methods (Bahdanau … General speaking, dialogue systems can be sorted into two classes (Serban et al., 2016): goal-driven represented by systems …

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 …

Natural Language Generation through Character-based RNNs with Finite-state Prior Knowledge.
R Goyal, M Dymetman, E Gaussier, U LIG – COLING, 2016 – aclweb.org
… (2015) in the context of a dialog system, where the input semantic representation is a Dialog Act (DA) … In the field of Neural Machine Translation (NMT), the problem of translating “rare words” such as named entities has recently attracted a fair amount of attention …

Generating Paraphrases from DBPedia using Deep Learning
A Sleimi, C Gardent – WebNLG 2016, 2016 – webnlg2016.sciencesconf.org
… 2014. Addressing the rare word problem in neural machine translation. arXiv preprint arXiv: 1410.8206. Ilya Sutskever, James Martens, and Geoffrey E Hin- ton. 2011 … 2015. Semantically conditioned ltsm-base natural lan- guage generation for spoken dialogue systems …

Knowledge as a teacher: Knowledge-guided structural attention networks
YN Chen, D Hakkani-Tur, G Tur, A Celikyilmaz… – arXiv preprint arXiv …, 2016 – arxiv.org
… Abstract Natural language understanding (NLU) is a core component of a spoken dialogue system. Recently … 1 Introduction In the past decade, goal-oriented spoken dialogue systems (SDS), such as the virtual personal assis- tants …

RNN-based Encoder-decoder Approach with Word Frequency Estimation
J Suzuki, M Nagata – arXiv preprint arXiv:1701.00138, 2016 – arxiv.org
… [2014] question answering Xu et al. [2015], dialogue system Vinyals and Le [2015], Shang et al. [2015], and abstractive summarization (ABS) Rush et al … Zhaopeng Tu, Zhengdong Lu, Yang Liu, Xiaohua Liu, and Hang Li. Modeling Cover- age for Neural Machine Translation …

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) …

Multi-modal variational encoder-decoders
IV Serban, II Ororbia, G Alexander, J Pineau… – arXiv preprint arXiv …, 2016 – arxiv.org
… Thus, for complex, multi-modal distributions — such as the distribution over topics in a text corpus, or natural language responses in a dialogue system — the uni-modal Gaussian prior inhibits the model’s ability to ex- tract and represent important structure in the data …

A Joint Model of Intent Determination and Slot Filling for Spoken Language Understanding.
X Zhang, H Wang – IJCAI, 2016 – pdfs.semanticscholar.org
… 1 Introduction Spoken language understanding (SLU) in human/machine spoken dialog systems aims to automatically identify the in- tent of the user as expressed in natural language and extract associated arguments or slots towards achieving a goal [Tur et al., 2011] …

Domain Adaptation for Neural Networks by Parameter Augmentation
Y Watanabe, K Hashimoto, Y Tsuruoka – arXiv preprint arXiv:1607.00410, 2016 – arxiv.org
… well. Wen et al. (2016) have proposed a procedure to generate natural language for multiple domains of spoken dialogue systems. They … data. However, the synthesis protocol is only applicable to the spoken dialogue system. In …

Transfer learning for cross-lingual sentiment classification with weakly shared deep neural networks
G Zhou, Z Zeng, JX Huang, T He – … of the 39th International ACM SIGIR …, 2016 – dl.acm.org
Page 1. Transfer Learning for Cross-Lingual Sentiment Classification with Weakly Shared Deep Neural Networks Guangyou Zhou1, Zhao Zeng1, Jimmy Xiangji Huang2, and Tingting He1 1 School of Computer, Central China …

Neural dialog state tracker for large ontologies by attention mechanism
Y Jang, J Ham, BJ Lee, Y Chang… – … Workshop (SLT), 2016 …, 2016 – ieeexplore.ieee.org
… Horii, “Convolutional neural networks for multi-topic dialog state tracking,” in Pro- ceedings of the 7th International Workshop on Spoken Dialogue Systems (IWSDS), 2016 … [13] Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Ben- gio, “Neural machine translation by jointly …

Sequence Generation & Dialogue Evaluation
R Lowe – cs.mcgill.ca
… Bahdanau, Cho, Bengio. “Neural Machine Translation by Jointly Learning to Align and Translate.” ICLR, 2015 … “How NOT to Evaluate Your Dialogue System: A Study of Unsupervised Evaluation Metrics for Dialogue Response Generation.” EMNLP, 2016 …

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
… Kyunghyun Cho (New York University) will give a talk on the latest research on neural machine translation without explicit linguistic structures … Since then, he held post-doc positions at the University of Edinburgh, work- ing on spoken dialogue systems, and the La Sapienza …

LSTM ENCODER–DECODER FOR DIALOGUE RESPONSE GENERATION
Z Yu, C Yuan, X Wang, G Yang – workshop.colips.org
… 2014: 3104-3112. [3] Jean, S., Cho, K., Memisevic, R., and Bengio, Y. On using very large target vocabulary for neural machine translation … Semantically conditioned lstm-based natural language generation for spoken dialogue systems[J]. arXiv preprint arXiv:1508.01745, 2015 …

Neural Discourse Modeling of Conversations
JM Pierre, M Butler, J Portnoff, L Aguilar – arXiv preprint arXiv:1607.04576, 2016 – arxiv.org
… rep., INESC-ID, 2013. [2] D. Bahdanau, K. Cho, and Y. Bengio. Neural machine translation by jointly learning to align and translate … The ubuntu dialogue corpus: A large dataset for research in unstructured multi-turn dialogue systems. arXiv preprint arXiv:1506.08909, 2015 …

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 generation[3] and so on … Neural machine translation by jointly learning to align and translate …

Stylistic Transfer in Natural Language Generation Systems Using Recurrent Neural Networks
J Kabbara, JCK Cheung – EMNLP 2016, 2016 – aclweb.org
… 2014a. On the proper- ties of neural machine translation: Encoder-decoder approaches. arXiv preprint arXiv: 1409.1259 … 2015. Seman- tically conditioned lstm-based natural language gener- ation for spoken dialogue systems …

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

The MSIIP system for dialog state tracking challenge 5
Y Su, M Li, J Wu – Spoken Language Technology Workshop …, 2016 – ieeexplore.ieee.org
… 525–539. [5] Li M and Wu J, “The MSIIP System for Dialog State Tracking Challenge 4,” in International Workshop on Spoken Dialog Systems (IWSDS), 2016. [6] Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Ben- gio, “Neural machine translation by jointly learning to align …

Recurrent Memory Addressing for describing videos
KK Agrawal, AK Jain, A Agarwalla, P Mitra – arXiv preprint arXiv …, 2016 – arxiv.org
Page 1. Recurrent Memory Addressing for describing videos Kumar Krishna Agrawal Arnav Kumar Jain Abhinav Agarwalla Pabitra Mitra Indian Institute of Technology Kharagpur {kumarkrishna, arnavkj95, abhinavagarawalla, pabitra}@iitkgp.ac.in Abstract …

Deep Recurrent Models with Fast-Forward Connections for Neural Machine Translation
JZYCX Wang, PLW Xu – pdfs.semanticscholar.org
… Neural machine translation (NMT) has attracted a lot of interests in solving the machine translation (MT) problems in recent years (Kalchbrenner and … Moreover, NMT models can also be easily adapted to other tasks such as dialog systems (Vinyals and Le, 2015), question …

Multilingual Multimodal Language Processing Using Neural Networks
MM Khapra, S Chandar – Proceedings of the 2016 Conference of the …, 2016 – aclweb.org
… 3. Multilingual/Multimodal Representation Learning [40 mins] 1. Using parallel data with and without word alignments 2. Using pivot view in the absence of parallel data 4. Multilingual/ Multimodal Generation [80 mins] 1. Neural Machine Translation systems 2 … dialog systems …

Improving Statistical Machine Translation with Target-Side Dependency Syntax
WR John – 2016 – repository.kulib.kyoto-u.ac.jp
… 2 1.2.2 Example-Based Machine Translation . . . . . 3 1.2.3 Statistical Machine Translation . . . . . 4 1.2.4 Neural Machine Translation . . . . . 8 1.3 Overview of Syntax-Based MT . . . . . 10 …

Translation (SedMT 2016)
D Xiong, K Duh, E Agirre, N Aranberri, H Wang – 2016 – anthology.aclweb.org
… Kyunghyun Cho (New York University) will give a talk on the latest research on neural machine translation without explicit linguistic structures … Since then, he held post-doc positions at the University of Edinburgh, work- ing on spoken dialogue systems, and the La Sapienza …

Guided Sequence-to-Sequence Learning with External Rule Memory
J Gu, B Hu, Z Lu, H Li, VOK Li – 2016 – openreview.net
… The transformation is similar to a conventional encoder-decoder model for machine transla- tion or dialogue system, hence a similar learning algorithm to maximise the log-likelihood … Neural machine translation by jointly learning to align and translate …

Visual Fashion-Product Search at SK Planet
T Kim, S Kim, S Na, H Kim, M Kim, BK Jeon – arXiv preprint arXiv …, 2016 – arxiv.org
… Cho, KyungHyun, van Merrienboer, Bart, Bahdanau, Dzmitry, and Bengio, Yoshua. On the properties of neural machine translation: Encoder-decoder approaches … Building end-to-end dialogue systems using generative hierarchical neural network models …

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
… Zara: A Virtual Interactive Dialogue System Incorporating Emotion, Sentiment and Personality Recognition Pascale Fung, Anik Dey, Farhad Bin Siddique, Ruixi Lin, Yang Yang, Dario … Kyoto-NMT: a Neural Machine Translation implementation in Chainer Fabien Cromieres xvi

Non-sentential Question Resolution using Sequence to Sequence Learning.
V Kumar, S Joshi – COLING, 2016 – aclweb.org
… References Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. CoRR, abs/1409.0473 … 2016. Building end- to-end dialogue systems using generative hierarchical neural network models. In AAAI …

Neural Networks for Natural Language Processing
L Mou – sei.pku.edu.cn
… is normalized Page 47. SequenceLevel Training ? Motivation: We don’t have the ground truth In a dialogue system, “The nature of of opendomain conversations shows that a variety of replies are plausible, but some are more meaningful, and others are not.” [21] …

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 …

Knowledge Enhanced Hybrid Neural Network for Text Matching
Y Wu, W Wu, Z Li, M Zhou – arXiv preprint arXiv:1611.04684, 2016 – arxiv.org
Page 1. Knowledge Enhanced Hybrid Neural Network for Text Matching Yu Wu†? , Wei Wu‡ , Zhoujun Li† , Ming Zhou‡ †State Key Lab of Software Development Environment, Beihang University, Beijing, China ‡ Microsoft …

Ranking Responses Oriented to Conversational Relevance in Chat-bots.
B Wu, B Wang, H Xue – COLING, 2016 – aclweb.org
… 4 Related Work Before open-domain chat agents, the task-completion oriented dialog system has been a subject of study for a long time, and most of these studies pay attention to particular vertical domains … Neural machine translation by jointly learning to align and translate …

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
… message-response matching. Introduction Human-computer conversation is a challenging task in AI and NLP. Existing conversation systems include task ori- ented dialog systems and non task oriented chatbots. The former aims …

A survey of voice translation methodologies—Acoustic dialect decoder
H Krupakar, K Rajvel, B Bharathi… – Information …, 2016 – ieeexplore.ieee.org
… Bengio, “Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation,” 2014. [20] K. Cho, B. van Merrienboer, D. Bahdanau, and Y. Bengio, “On the Properties of Neural Machine Translation: Encoder-Decoder Approaches,” 2014 …

1 Situation Intelligence Framework
Z Yu – cs.cmu.edu
… K. Cho, and Y. Bengio, “Neural machine translation by jointly learning to align and translate,” ArXiv preprint arXiv:1409.0473, 2014. [2] Y.-N. Chen, WY Wang, and AI Rudnicky, “Unsupervised induction and filling of semantic slots for spoken dialogue systems using frame …

Multi-label learning with the RNNs for Fashion Search
T Kim – 2016 – openreview.net
… KyungHyun Cho, Bart van Merrienboer, Dzmitry Bahdanau, and Yoshua Bengio. On the properties of neural machine translation: Encoder-decoder approaches … Building end-to-end dialogue systems using generative hierarchical neural network models …

Multimodal Memory Modelling for Video Captioning
J Wang, W Wang, Y Huang, L Wang, T Tan – arXiv preprint arXiv …, 2016 – arxiv.org
… The third class of meth- ods inspired by Neural Machine Translation (NMT) [19, 6] map video sequence to sentence by virtue of recent deep … need long-term dependency modelling, eg, textual ques- tion answering [3, 14], visual question answering [39] and dialog systems [8]. As …

Neural Paraphrase Generation with Stacked Residual LSTM Networks
A Prakash, SA Hasan, K Lee, V Datla, A Qadir… – arXiv preprint arXiv …, 2016 – arxiv.org
… for example, in the areas of machine translation (Cho et al., 2014; Bahdanau et al., 2015), speech recognition (Li and Wu, 2015), language modeling (Vinyals et al., 2015), and dialogue systems (Serban et al … Neural Machine Translation by Jointly Learning to Align and Translate …

Topic Augmented Neural Network for Short Text Conversation.
Y Wu, W Wu, Z Li, M Zhou – CoRR, 2016 – pdfs.semanticscholar.org
Page 1. Topic Augmented Neural Network for Short Text Conversation Yu Wu†? , Wei Wu‡ , Zhoujun Li† , Ming Zhou‡ †State Key Lab of Software Development Environment, Beihang University, Beijing, China ‡ Microsoft Research …

Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing
J Su, K Duh, X Carreras – Proceedings of the 2016 Conference on …, 2016 – aclweb.org
… Speech-based and Multimodal Approaches for Human versus Computer Addressee Detection Abstract: As dialog systems become ubiquitous, we must learn how to detect when a system is spoken to, and avoid mistaking human-human speech as computer-directed input …

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
… 30 Controlling Politeness in Neural Machine Translation via Side Constraints Rico Sennrich, Barry Haddow and Alexandra Birch … Multi-domain Neural Network Language Generation for Spoken Dialogue Systems Tsung-Hsien Wen, Milica Gašic, Nikola Mrkšic, Lina M. Rojas …

Review of state-of-the-arts in artificial intelligence. Present and future of AI.
V Shakirov – alpha.sinp.msu.ru
… ”Neural Machine Translation by Jointly Learning to Align and Translate” http://arxiv.org/abs/ 1409.0473 [17] Oriol Vinyals, Quoc Le … ”Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models” http:// arxiv.org/abs/1507.04808 …

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
… 66 Modeling Coverage for Neural Machine Translation Zhaopeng Tu, Zhengdong Lu, Yang Liu, Xiaohua Liu and Hang Li … 76 Improving Neural Machine Translation Models with Monolingual Data Rico Sennrich, Barry Haddow and Alexandra Birch …

Knowledge as a Teacher: Knowledge-Guided Structural Attention Networks
A Celikyilmaz, J Gao, L Deng – researchgate.net
… Abstract Natural language understanding (NLU) is a core component of a spoken dialogue system. Recently … 1 Introduction In the past decade, goal-oriented spoken dialogue systems (SDS), such as the virtual personal assis- tants …

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 …

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
… ”Neural Machine Translation by Jointly Learning to Align and Translate” http://arxiv.org/abs/ 1409.0473 [22] Oriol Vinyals, Quoc Le … ”Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models” http:// arxiv.org/abs/1507.04808 …

Gaussian Attention Model and Its Application to Knowledgebase Embedding and Question Answering
L Zhang, J Winn, R Tomioka – arXiv preprint arXiv:1611.02266, 2016 – arxiv.org
Page 1. Under review as a conference paper at ICLR 2017 GAUSSIAN ATTENTION MODEL AND ITS APPLICATION TO KNOWLEDGE BASE EMBEDDING AND QUESTION ANSWERING Liwen Zhang Department of Computer …

A learned representation for artistic style
V Dumoulin, J Shlens, M Kudlur, A Behboodi… – arXiv preprint arXiv …, 2016 – arxiv.org

A Corpus for Event Localization
C Ward – 2016 – bir.brandeis.edu
Page 1. A Corpus for Event Localization Master’s Thesis Presented to The Faculty of the Graduate School of Arts and Sciences Brandeis University Department of Computer Science Professor James Pustejovsky, Advisor In Partial Fulfillment of the Requirements for the Degree …

Learning to Interpret and Generate Instructional Recipes
C Kiddon – 2016 – digital.lib.washington.edu
… 69 4.5 Dialogue System Results … Laroche et al. (2013) proposed Cooking Coach, a spoken dialogue system to help a user search for recipes and prepare the recipe. A similar, if not more futuristic, application is the construction of robotic cooking assistants (Beetz …

On bridging the semantic gap in knowledge-based question answering
P Yin? ??? – HKU Theses Online (HKUTO), 2016 – hub.hku.hk
Page 1. Title On bridging the semantic gap in knowledge-based question answering Author(s) Yin, Pengcheng; ??? Citation Yin, P. [???]. (2016). On bridging the semantic gap in knowledge-based question answering. (Thesis) …

Computer Vision and Natural Language Processing: Recent Approaches in Multimedia and Robotics
P Wiriyathammabhum, D Summers-Stay… – ACM Computing …, 2016 – dl.acm.org
Page 1. 71 Computer Vision and Natural Language Processing: Recent Approaches in Multimedia and Robotics PERATHAM WIRIYATHAMMABHUM, University of Maryland, College Park DOUGLAS SUMMERS-STAY, US …

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 …

From predictive to interactive multimodal language learning
A Lazaridou – 2016 – eprints-phd.biblio.unitn.it
Page 1. From predictive to interactive multimodal language learning by Angeliki Lazaridou Submitted to the Center for Mind and Brain Sciences (CiMeC) in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the UNIVERSITY OF TRENTO …

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