Conditional Random Fields & Dialog Systems

Notes:

Conditional random fields (CRFs) are a type of statistical model that can be used for predicting structured outputs, such as sequences or sets of discrete labels, based on input data. They are often used in pattern recognition and machine learning tasks, such as natural language processing and computer vision. CRFs are based on the principle of maximum entropy, which states that the probability distribution that is most consistent with the known information is the one that has the highest entropy. In a CRF, the output is modeled as a conditional distribution over a set of possible labels, given the input data. The model is trained using a labeled training dataset, in which the input data and the corresponding correct output labels are known. The trained model can then be used to make predictions for new input data by estimating the most likely output labels based on the probabilities learned during training. CRFs are useful for tasks where the output has a structured relationship with the input data, such as in natural language processing where the output labels may correspond to part-of-speech tags for words in a sentence.

Conditional random fields (CRFs) can be used in dialog systems to predict the next words or actions in a conversation based on the previous context. In a dialog system, the input data would be the previous words or actions in the conversation, and the output labels would be the possible next words or actions. The CRF model can be trained on a large dataset of conversation transcripts, where the input data and the corresponding correct output labels are known. The trained model can then be used to predict the next words or actions in a conversation by estimating the most likely output label based on the probabilities learned during training. This can be useful for generating appropriate responses in a conversation, or for predicting the next steps in a task-based dialog system. CRFs can also be used to model the structure of the dialog, such as the transitions between different dialog states or the dependencies between different words or actions in the conversation.

  • Computational morphology is a field of computational linguistics that deals with the study of the internal structure of words and how they are formed from smaller units of meaning (morphemes). It involves the development of algorithms and techniques for analyzing and processing the morphological structure of words in natural language text.
  • Hypotheses ranking is the process of ordering or ranking different hypotheses or predictions based on their likelihood or probability of being correct. In the context of natural language processing, hypotheses ranking may involve ranking different possible interpretations or translations of a given text or speech based on their likelihood of being the correct one. Hypotheses ranking can be used to improve the accuracy of natural language processing systems by choosing the most likely hypothesis or interpretation among a set of candidates. It may involve using statistical or machine learning techniques to evaluate the likelihood of different hypotheses based on the available data and evidence.

Resources:

Wikipedia:

References:

See also:

POMDP (Partially Observable Markov Decision Process) & Dialog Systems

Deep Learning in Natural Language Processing
L Deng, Y Liu – 2018 – books.google.com
… He, Dilek Hakkani-Tür and Li Deng 3 Deep Learning in Spoken and Text-Based Dialog Systems … understanding Convolutional neural network Convolutional neural network based semantic model Community question answering Conditional random field Collaborative topic …

On the effects of using word2vec representations in neural networks for dialogue act recognition
C Cerisara, P Kral, L Lenc – Computer Speech & Language, 2018 – Elsevier
… Yahya et al., 2010), BayesNet (Petukhova and Bunt, 2011), Neural Networks (Levin et al., 2003), but also Boosting (Tur et al., 2006), Maximum Entropy Models (Ang et al., 2005), Conditional Random Fields (Quarteroni et … Nature, Human-human, Human-human, Bot(WoZ)-human …

CCG supertagging via Bidirectional LSTM-CRF neural architecture
R Kadari, Y Zhang, W Zhang, T Liu – Neurocomputing, 2018 – Elsevier
… models. In this paper, we use the combination of Conditional Random Field and Bidirectional Long Short-Term Memory models … Afterward, the model uses sentence level tag information thanks to Conditional Random Field model. By …

Alquist: The alexa prize socialbot
J Pichl, P Marek, J Konrád, M Matulík… – arXiv preprint arXiv …, 2018 – arxiv.org
… used as a tourist guide [5], reservation assistants [6] or technical support bots [1], because … This allows the bot to retrieve the context even if the session was interrupted … We also trained Conditional Random Fields (CRF) sequence labeling using data which were generated using …

Natural Language Processing for Industry
S Quarteroni – Informatik-Spektrum, 2018 – Springer
… Cases such as pizza-ordering bots have become … either pattern-based techniques (dictio- naries, regular expressions) or machine learning approaches (eg Conditional Random Fields) … APIs include Microsoft’s cognitive services (for STT, NLU and TTS) and bot framework (for …

Role play-based question-answering by real users for building chatbots with consistent personalities
R Higashinaka, M Mizukami, H Kawabata… – Proceedings of the 19th …, 2018 – aclweb.org
… next step is to determine if the col- lected pairs can be useful for creating chatbots that exhibit … and the answer part of the most relevant pair is returned as a chatbot’s response … To extract such NPs from an utter- ance, we used conditional random fields (CRFs) (Lafferty et al., 2001 …

Building Advanced Dialogue Managers for Goal-Oriented Dialogue Systems
V Ilievski – arXiv preprint arXiv:1806.00780, 2018 – arxiv.org
… Goal-Oriented bots contain an initial NLU component, that is tasked with deter- mining the … The User Simulator creates a user – bot conversation, given the semantic frames … implemented as an LSTM-based recurrent neural network with a Conditional Random Field (CRF) layer …

Conversational Modelling for ChatBots: Current Approaches and Future Directions
M McTear – 2018 – spokenlanguagetechnology.com
… is treated as a sequence classification problem for which techniques such as Conditional Random Fields are used [4 … As an alternative the Bot Builder SDK for Node.js makes use of a Waterfall … of the same skill (eg weath- er forecast) may behave differently on the same chatbot …

Towards Dialogue-Based Navigation with Multivariate Adaptation Driven by Intention and Politeness for Social Robots
C Bothe, F Garcia, AC Maya, AK Pandey… – … on Social Robotics, 2018 – Springer
… The probabilistic parser uses logistic regression for intent classification and conditional random fields (CRFs) for slot filling … Suddrey, G., Jacobson, A., Ward, B.: Enabling a pepper robot to provide automated and interactive tours of a robotics laboratory …

Early prediction for physical human robot collaboration in the operating room
T Zhou, JP Wachs – Autonomous Robots, 2018 – Springer
… Machine learning techniques have been applied to recognize turn-taking events automatically, mainly for spoken dialog systems … extended to mul- timodal end-of-turn detection in multi-party meetings using Conditional Random Field (De Kok … Auton Robot (2018) 42:977–995 …

Estimating interviewee’s willingness in multimodal human robot interview interaction
T Ishihara, K Nitta, F Nagasawa, S Okada – Proceedings of the …, 2018 – dl.acm.org
… In Proc. Int’l Workshop Spoken Dialogue Systems (IWSDS) … 2004. Applying Conditional Random Fields to Japanese Morphological Analysis … 2017. Fully automatic analysis of engagement and its relationship to personality in human-robot interactions …

Lead Engagement by Automated Real Estate Chatbot
T Quan, T Trinh, D Ngo, H Pham… – … on Information and …, 2018 – ieeexplore.ieee.org
… In [5], Hidden Markov Models (HMM) and Conditional Random Field (CRF) is used to … Even though chatbots cannot fully replace the traditional relation between agents and home buyers, they … V. EXPERIMENTS The accuracy of our chatbot heavily relies on its capability of intent …

Towards Understanding User Requests in AI Bots
OT Tran, TC Luong – Pacific Rim International Conference on Artificial …, 2018 – Springer
… [4] built a bot answering a … 93.08% in layer 1, and the F-measure of 92.97% in layer 2. This result is very promising to help bots better understand … Lafferty, JD, McCallum, A., Perera, FCN: Conditional random fields: probabilistic models for segmenting and labeling sequence data …

Using Reinforcement Learning for Dialogue Act Classification in Task-oriented Conversation Systems
Q Xia – DEStech Transactions on Computer Science and …, 2018 – dpi-proceedings.com
… Siri and Cortana, shown in figure 1, are also typical applications of chatting robot. Siri is a dialogue system product developed by Apple Company and it is widely … vector machines(SVM), hidden Markov models(HMM), graphical models, conditional random fields(CRF), maximum …

Text, Speech, and Dialogue: 21st International Conference, TSD 2018, Brno, Czech Republic, September 11-14, 2018, Proceedings
P Sojka, A Horák, I Kope?ek, K Pala – 2018 – books.google.com
… Disambiguation and Segmentation for Historical Polish with Graph-Based Conditional Random Fields … Patterns Implicitly Using Joining-in-Type Robot-Assisted Language … to Interrupt the User at the Right Time in Incremental Dialogue Systems …

MDKB-Bot: A Practical Framework for Multi-Domain Task-Oriented Dialogue System
Y Lao, W Liu, S Gao, S Li – data-intelligence-journal.org
… For example, Xu and Sarikaya [1] applied a RNN to perform contextual domain classification and used a triangular conditional random field (CRF) based on a convolutional neural network … 3 MDKB-Bot: A Practical Framework for Multi-Domain Task-Oriented Dialogue System …

Exploring the importance of context and embeddings in neural NER models for task-oriented dialogue systems
P Jayarao, C Jain, A Srivastava – arXiv preprint arXiv:1812.02370, 2018 – arxiv.org
… We define a task-oriented dialogue system where a user and the system take turns ex- changing information till some concluding … Any task ori- ented bot must figure out the correct intent and fill slots for requested task interactively until all re … 4.4 Conditional Random Fields (CRF) …

A Unified Neural Architecture for Joint Dialog Act Segmentation and Recognition in Spoken Dialog System
T Zhao, T Kawahara – Proceedings of the 19th Annual SIGdial Meeting …, 2018 – aclweb.org
… As an es- sential part of spoken dialog system (SDS), SLU analyzes user input, and … tation and recognition are based on non-network models such as Conditional Random Field (CRF) … work was supported by JST ERATO Ishig- uro Symbiotic Human-Robot Interaction program …

Towards Building a Domain Independent Dialog System
P Jwalapuram – 2018 – web2py.iiit.ac.in
… We collected a few system generated dialogs from popular conversational chatbots across the … improved the accuracies of the rule based system by using Conditional Random Fields to label … A universal chatbot evaluation system using dialog efficiency, dialog quality and user …

Enhancing Inquisitiveness of Chatbots Through NER Integration
S Reshmi, K Balakrishnan – 2018 International Conference on …, 2018 – ieeexplore.ieee.org
… in this regard has paved the way for the development of many other bots … The tool provides a general implementation of linear chain based conditional random field (CRF) sequence models … When the user responds, the bot needs to ensure whether the user has entered a …

Effect of Mutual Self-Disclosure in Spoken Dialog System on User Impression
S Tada, Y Chiba, T Nose, A Ito – Proceedings, APSIPA Annual Summit …, 2018 – apsipa.org
… [15] proposed a method using the conditional random field (CRF) and phrase-level features for the focus de- tection … [6] Z. Miyashita, T. Kanda, M. Shiomi, H. Ishiguro, and N. Hagita, “A robot in a shopping mall that affectively guide customers”, Journal of Robotics Society of …

An Overview of Text Generation Technology
Z Xu, X Wu – ijklp.org
… in the summary[3], the summary task can be treated as a sequential tagging task and the conditional random field can be … customer service[39] and chat robot[40] … the Seq2Seq model to generate a question and answer set in the IT domain, and generates a dialog system upon …

EmotionX-Area66: Predicting Emotions in Dialogues using Hierarchical Attention Network with Sequence Labeling
R Saxena, S Bhat, N Pedanekar – … of the Sixth International Workshop on …, 2018 – aclweb.org
… With the advent of social media and dialogue systems like personal assistants and chatbots … et al., 2017) have captured dependencies in utterances for dialogue act classification using Hidden Markov Model (HMM) and Conditional Random Field (CRF) respectively …

Proceedings of the 22nd Conference on Computational Natural Language Learning
A Korhonen, I Titov – Proceedings of the 22nd Conference on …, 2018 – aclweb.org
… xix Embedded-State Latent Conditional Random Fields for Sequence Labeling Dung Thai, Sree Harsha … Model for Low-Resource Natural Language Generation in Dialogue Systems Van-Khanh … Churn Intent Detection in Multilingual Chatbot Conversations and Social Media …

Engagement Recognition in Spoken Dialogue via Neural Network by Aggregating Different Annotators’ Models
K Inoue, D Lala, K Takanashi… – Proc. Interspeech …, 2018 – isca-speech.org
… These models used conditional random fields (CRF), while our model uses neural networks, with the advan … and R. Simmons, “A spatial model of engagement for a social robot,” in International … and E. Horvitz, “Learning to predict engagement with a spoken dialog system in open …

Chatbol, a chatbot for the Spanish “La Liga”
C Segura, A Palau, J Luque, M Costa-jussa, R Banchs – 2018 – oar.a-star.edu.sg
… course recommendations; the entertainment domain, such a Stark Trek system [10] or Humorist Bot that can … 2 https://toni.football/ 3 https://botlist.co/bots/tokabot-football … Finally, a conditional random field classifier is trained on the sentence tokens and POS tags to extract entities …

Dialogue Scenario Collection of Persuasive Dialogue with Emotional Expressions via Crowdsourcing
K Yoshino, Y Ishikawa, M Mizukami, Y Suzuki… – Proceedings of the …, 2018 – aclweb.org
… 5 John accepts the suggestion of the robot Does the sentence contains expressions of clear acceptance … Ap- plying conditional random fields to japanese morpholog- ical analysis … Chatbot evaluation and database expansion via crowd- sourcing …

Concorde: Morphological Agreement in Conversational Models
D Polykovskiy, D Soloviev… – Asian Conference on …, 2018 – proceedings.mlr.press
… conversational models are widely used in applications such as personal assistants and chat bots … a wide range of applications, from simple rule-based chatbots to complex … and modern context-sensitive approaches to lemmatization based on conditional random fields (CRF) or …

Implementation of A Neural Natural Language Understanding Component for Arabic Dialogue Systems
AM Bashir, A Hassan, B Rosman, D Duma… – Procedia computer …, 2018 – Elsevier
… T Sys sati an airl task F sho suc bot F wo vec tag D usin … Other techniques benefit from the relation between sequence labels to enhance tagging using Conditional Random Fields (CRF) [9 … and University of North Dakota [12] Abu Ali D, Habash N. Botta: An Arabic Dialect Chatbot …

Multimodal dialogue system evaluation: a case study applying usability standards
A Malchanau, V Petukhova, H Bunt – colips.org
… The sequence learning Conditional Random Field (CRF) models were trained to predict three types of classes … Inter- national journal of social robotics, 1(1), pp.71-81 (2009) 17 … Kooijmans, T., Kanda, T., Bartneck, C., Ishiguro, H., Hagita, N.: Accelerating Robot Develop- ment …

Advancing the State of the Art in Open Domain Dialog Systems through the Alexa Prize
C Khatri, B Hedayatnia, A Venkatesh, J Nunn… – arXiv preprint arXiv …, 2018 – arxiv.org
… took the standard approach by using a profanity checker before returning responses from bots … data which they annotated themselves and once abuse towards their bot was detected … They trained and tested a Conditional Random Field (CRF)-based sequence model on specific …

Engagement Recognition based on Multimodal Behaviors for Human-Robot Dialogue
K Inoue – 2018 – repository.kulib.kyoto-u.ac.jp
… Chapter 1 describes the background of this thesis in the context of spoken dialogue systems and human-robot interaction in order to clarify problems and approaches. Chapter 2 … continuously supported me to implement the spoken dialogue system for the android robot ERICA …

Context-Aware Dialog Re-Ranking for Task-Oriented Dialog Systems
J Ohmura, M Eskenazi – arXiv preprint arXiv:1811.11430, 2018 – arxiv.org
… Hakkani-Tür, “Semantic parsing using word confusion networks with conditional random fields.,” in INTERSPEECH … Informa- tion Processing Systems 26, CJC Burges, L. Bot- tou, M … Hui Xue, “Ranking responses oriented to conversational relevance in chat- bots,” in Proceedings …

Natural Language Processing for Industrial Applications
S Quarteroni – Spektrum, 2018 – meine.gi.de
… Cases such as pizza-ordering bots have become DOI 10.1007/s00287-018 … based techniques (dictio- naries, regular expressions) or machine learning approaches (eg Conditional Random Fields) … Microsoft’s cognitive services (for STT, NLU and TTS) and bot framework (for DM …

Latent character model for engagement recognition based on multimodal behaviors
K Inoue, D Lala, K Takanashi, T Kawahara – … Spoken Dialogue Systems  …, 2018 – colips.org
… similar model considering the difference of annotators is a two-step conditional random fields (CRF), which … D., Horvitz, E.: Learning to predict engagement with a spoken dialog system in open … I., Paiva, A., McOwan, PW: Detecting user engagement with a robot companion using …

Learning factorized multimodal representations
YHH Tsai, PP Liang, A Zadeh, LP Morency… – arXiv preprint arXiv …, 2018 – arxiv.org
… across multiple modalities. It is a chal- lenging yet crucial research with real-world applications such as robotics [1; 30], dialogue sys- tems [42; 43], intelligent tutoring systems [60; 31; 41], and healthcare diagnosis [38; 13]. At the …

Analyzing assumptions in conversation disentanglement research through the lens of a new dataset and model
JK Kummerfeld, SR Gouravajhala, J Peper… – arXiv preprint arXiv …, 2018 – arxiv.org
… 2013c). In follow up work, they identified messages relevant to the Unity desk- top environment (Uthus and Aha, 2013b), and whether questions can be answered by the chan- nel bot alone (Uthus and Aha, 2013a). Lowe et al …

Emory IrisBot: An Open-Domain Conversational Bot for Personalized Information Access
A Ahmadvand, IJ Choi, H Sahijwani, J Schmidt, M Sun… – dex-microsites-prod.s3.amazonaws …
… a general catch-all Chat component, which was based on the ALICE chatbot, implemented in … We believe this is an important innovation as it allows our bot to tailor the … propose the next interesting topic for each customer, we introduce a Conditional Random Field (CRF)-based …

A Survey of Knowledge Representation and Retrieval for Learning in Service Robotics
D Paulius, Y Sun – arXiv preprint arXiv:1807.02192, 2018 – arxiv.org
… on learning approaches which make use of probabilistic models, taking the approach that the robot’s world and … discrete and reflect observed phenomena within a setting or environment when applied to robotics; nodes usually … Figure 3: An example of conditional random fields …

Engagement recognition by a latent character model based on multimodal listener behaviors in spoken dialogue
K Inoue, D Lala, K Takanashi… – APSIPA Transactions on …, 2018 – cambridge.org
… demon- strate an online processing of engagement recognition for spoken dialogue systems in Section V … A chatbot system was implemented to select a dialogue module according to user … group investigated how to handle user disengagement in a human–robot interaction [20] …

Customization of an example-based dialog system with user data and distributed word representations
E Seto, R Nishimura, N Kitaoka – 2018 Asia-Pacific Signal and …, 2018 – ieeexplore.ieee.org
… [2] S. Han, K. Lee, D. Lee, and GG Lee, “Counseling dialog system with 5w1h … Y. Huang, and H.-Y. Lee, “Mitigating the impact of speech recognition errors on chatbot using sequence … [19] T. Kudo, K. Yamamoto, and Y. Matsumoto, “Applying conditional random fields to Japanese …

Unsupervised Dialogue Act Classification with Optimum-Path Forest
LCF Ribeiro, JP Papa – 2018 31st SIBGRAPI Conference on …, 2018 – ieeexplore.ieee.org
… problem for other tasks, such as the development of chatbots where the … [31] obtained 79.2% accuracy using bidirectional LSTM with Conditional Random Fields (CRF) … E. Dialog System Technology Challenges datasets The Dialog System Technology Challenges, previously Di …

Slot-gated modeling for joint slot filling and intent prediction
CW Goo, G Gao, YK Hsu, CL Huo, TC Chen… – Proceedings of the …, 2018 – aclweb.org
… Spoken language understanding (SLU) is a criti- cal component in spoken dialogue systems … Popular approaches for slot fill- ing include conditional random fields (CRF) (Ray … Intent Utterance Example SearchCreativeWork Find me the I, Robot television show GetWeather Is it …

Comparison of Speech Recognition Performance Between Kaldi and Google Cloud Speech API
T Kimura, T Nose, S Hirooka, Y Chiba, A Ito – International Conference on …, 2018 – Springer
… CrossRefGoogle Scholar. 5. Kudo, T., Yamamoto, K., Matsumoto, Y.: Applying conditional random fields to Japanese … S., Leuski, A., Traum, DR: Which ASR should I choose for my dialogue system … 978-3-030-03748-2; eBook Packages Intelligent Technologies and Robotics …

Using Lexical Alignment and Referring Ability to Address Data Sparsity in Situated Dialog Reference Resolution
T Shore, G Skantze – Proceedings of the 2018 Conference on Empirical …, 2018 – aclweb.org
Page 1. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2288–2297 Brussels, Belgium, October 31 – November 4, 2018. c 2018 Association for Computational Linguistics 2288 …

Arsas: An arabic speech-act and sentiment corpus of tweets
ARA Elmadany, WM Hamdy Mubarak – … 3: The 3rd Workshop on Open …, 2018 – lrec-conf.org
… eown et al., 2007), question answering (Hong and Davison, 2009), and chat-bots (Feng et … the TuDiCoI corpus to develop a discriminative algorithm based on conditional random fields (CRF) to … An in- telligent discussion-bot for answering student queries in threaded discussions …

Virtual Doctor: An Intelligent Human-Computer Dialogue System for Quick Response to People in Need
S Mallios – 2018 – corescholar.libraries.wright.edu
… 2018 Virtual Doctor: An Intelligent Human-Computer Dialogue System for Quick Response to People in Need … Repository Citation Mallios, Stavros, “Virtual Doctor: An Intelligent Human-Computer Dialogue System for Quick Response to People in Need” (2018) …

Data-Driven Language Understanding for Spoken Dialogue Systems
N Mrkši? – 2018 – repository.cam.ac.uk
… information (Henderson et al., 2014b).2 1This term is used interchangeably with goal-oriented dialogue systems. 2Alternative chat-bot style systems do not make use of task ontologies or the pipeline model. Instead, these models …

Spiking Neural Networks for Early Prediction in Human Robot Collaboration
T Zhou, JP Wachs – arXiv preprint arXiv:1807.11096, 2018 – arxiv.org
… et al., 2015), Decision Trees (DT) (Saito et al., 2015) and Conditional Random Field (CRF) (De … Those signals were fed to the TTSNet framework to guide the movement of the robot … In spoken dialogue systems, turn-taking is detected by finding short pauses (usually between 0.5 …

Generating More Interesting Responses in Neural Conversation Models with Distributional Constraints
A Baheti, A Ritter, J Li, B Dolan – arXiv preprint arXiv:1809.01215, 2018 – arxiv.org
… Such re- sponse generation models could be combined with traditional dialogue systems to enable more natu- ral and adaptive conversation, in addition to new applications such as predictive response sugges- tion (Kannan et al., 2016), however many chal- lenges remain …

Conversational recommender system
Y Sun, Y Zhang – The 41st International ACM SIGIR Conference on …, 2018 – dl.acm.org
… To improve the success or conversion rate of a shopping/sales chatbot, we argue that one should integrate recommendation tech- niques into conversational systems … [13] used LSTM and Conditional Random Fields networks to … [1] built an end-to-end task oriented bot based on …

State-of-the-Art Approaches for German Language Chat-Bot Development
N Boisgard – 2018 – ec.tuwien.ac.at
… Figure 1.2: The number of publications containing the terms “chatbot”, “chatterbot”, “chat-bot” or “chat bot … 2.1: A list of the terms proposed as synonymous to the term “chat-bot” in the … as in lieu with the classification used in [Jurafsky and Martin, 2017a], chat-bots being merely chit …

Explicit State Tracking with Semi-Supervisionfor Neural Dialogue Generation
X Jin, W Lei, Z Ren, H Chen, S Liang, Y Zhao… – Proceedings of the 27th …, 2018 – dl.acm.org
… Numerous dialog state tracking mechanisms with a limited state space have been proposed for task-oriented dialogue systems, eg, hand-crafted rules [12, 47], conditional random fields [18, 19, 34], maximum entropy [51], and neural networks [15] …

An Adaptive Conversational Bot Framework
IC Etinger – arXiv preprint arXiv:1808.09890, 2018 – arxiv.org
… Additionally, sample bots provided and found online are too simple to test reliability … It produces a conversational bot that extracts meaningful information from user’s sentences … J. Glass, A Conversational Movie Search System Based on Conditional Random Fields, isca-speech …

Human computer interaction research through the lens of a
K Koumaditis, T Hussain – Lecture Notes in Computer Science – pure.au.dk
… vant for the design of spoken dialog systems (Prylipko et al., 2014) and inves- tigate the … J., & Castrillon, M. (2004) Useful computer vision techniques for human-robot interaction. Vol … Conditional random fields for web user task recognition based on human computer interaction …

Modeling multi-turn conversation with deep utterance aggregation
Z Zhang, J Li, P Zhu, H Zhao, G Liu – arXiv preprint arXiv:1806.09102, 2018 – arxiv.org
… Matching Score Matching Score Matching Score {context Matching Attention Flow Figure 2: Structure overview of the proposed dialogue system. 3 Deep Utterance Aggregating Strategy … Bot: Nuts is good … 2017. Superagent: A customer service chatbot for e-commerce websites …

On Character vs Word Embeddings as Input for English Sentence Classification
J Hammerton, M Vintró, S Kapetanakis… – Proceedings of SAI …, 2018 – Springer
… as question answering and dialogue systems. Several approaches have been proposed for the problem of short-text classification, which leverage traditional machine learning techniques such as Support Vector Machines (SVMs) [6] and Conditional Random Fields (CRFs) [7 …

Task-Oriented Dialog Agents Using Memory-Networks and Ensemble Learning
RFG Meléndez – 2018 – pdfs.semanticscholar.org
… Rasa NLU uses a Conditional Random Field (CRM) which can generalize to detect unknown entity values … hyper-parameter optimization, arguably because of the differences between the train and test bots … An action template is a bot utterance with some slots to be filled in after …

Audio-visual word prominence detection from clean and noisy speech
M Heckmann – Computer Speech & Language, 2018 – Elsevier
… For spoken dialog systems one situation where the prosodic information is particularly important is … emotions or recorded when children were interacting with a small robot were used … Mixture Model (GMM), a Support Vector Machine (SVM), a Conditional Random Field (CRF) and …

Data-Driven Input Feature Augmentation for Named Entity Recognition
??? – 2018 – s-space.snu.ac.kr
… 13 2.2.1 The Conditional Random Field … language understanding (SLU) component of dialog management systems, or ”chatbots”, along with the intent detection classification task … For example, in an automatic restaurant search query system, the dialog system may have …

A Context-based Approach for Dialogue Act Recognition using Simple Recurrent Neural Networks
C Bothe, C Weber, S Magg, S Wermter – arXiv preprint arXiv:1805.06280, 2018 – arxiv.org
… utterance representations are further used to classify DA classes using the conditional random field (CRF) as … be adapted to an online learning tool such as a spoken dialogue system where one … This makes our model suitable for human-robot/computer interaction which can be …

Laughbot: Detecting Humor in Spoken Language with Language and Audio Cues
K Park, A Hu, N Muenster – Future of Information and Communication …, 2018 – Springer
… Chatbots Spoken natural language processing Deep learning Machine learning. Download conference … into three models: convolutional neural network (CNN), RNN, and conditional random field (CRF), and … our research and testing, predicting humor using a chatbot-style audio …

Learning socio-communicative behaviors of a humanoid robot by demonstration
DC Nguyen – 2018 – hal.archives-ouvertes.fr
… 1.3.1 Developmental Robotics … In this chapter, we also illustrate how LSTM and Conditional Random Field (CRF) can be used to generate verbal back-channels from … chapter proposes to adapt the RL/RI scenario in order to cope with the limitations of the robot’s motor cabilities …

Multimodal-multisensor affect detection
SK D’Mello, N Bosch, H Chen – The Handbook of Multimodal-Multisensor …, 2018 – dl.acm.org
… 180. 2. T. Baltrušaitis, N. Banda, and P. Robinson. 2013. Dimensional Affect Recognition using Continuous Conditional Random Fields … 2013. Attention and Emotion Based Adaption of Dialog Systems … 2012. Music-aided affective interaction between human and service robot …

Domain Knowledge Driven Key Term Extraction for IT Services
P Mohapatra, Y Deng, A Gupta, G Dasgupta… – … Conference on Service …, 2018 – Springer
… graph, which provides context information and inference capabilities for search and chat-bots … of cognitive applications on top of search, chat-bot/dialog systems, and knowledge … A., Li, W.: Early results for named entity recognition with conditional random fields, feature induction …

A Joint Introduction to Natural Language Processing and to Deep Learning
L Deng, Y Liu – Deep Learning in Natural Language Processing, 2018 – Springer
… More specifically, for the dialogue policy component of dialogue systems, powerful reinforcement … 2011) to discriminative models like conditional random fields (Tur and Deng 2011) … recognition, medical informatics, advertisement, medical image analysis, robotics, self-driving …

Data-driven development of Virtual Sign Language Communication Agents
A Balayn, H Brock, K Nakadai – … Robot and Human Interactive …, 2018 – ieeexplore.ieee.org
… consecutive signs recognize short sentence expressions in Chinese SL with conditional random fields with 90 … lower performances than recent works but they are more portable on robotics platforms since … positions and the torques applied to each joint of the robot between two …

Towards Understanding Code-Mixed Telugu-English Data
DS Jitta – 2018 – web2py.iiit.ac.in
… Though code-mixing is the most natural form of conversation both in speech and text (online chats), the current dialog systems and search engines are not capable of handling this kind of social interaction … viii Page 9. CONTENTS ix 4.2 Dialog System and its Components …

Word Segmentation From Phoneme Sequences Based On Pitman-Yor Semi-Markov Model Exploiting Subword Information
R Takeda, K Komatani… – 2018 IEEE Spoken …, 2018 – ieeexplore.ieee.org
… 1. When a robot listens to a human utterance that includes unknown vocabulary like … 2) the dynamic increase in new vocabulary, which is important for online dialogue systems … used an integrated conditional random field (CRF) and PYSMM for semi-supervised segmentation of …

An Efficient Framework for Development of Task-Oriented Dialog Systems in a Smart Home Environment
Y Park, S Kang, J Seo – Sensors, 2018 – mdpi.com
… Neural Network-Based Embarrassing Situation Detection under Camera for Social Robot in Smart … Figure 1 shows the typical structure of a task-oriented dialog system … 13], support vector machine (SVM) [14], maximum entropy (ME) [15], and conditional random fields (CRF) [16 …

Reinforcement Learning and Reward Estimation for Dialogue Policy Optimisation
PH Su – 2018 – repository.cam.ac.uk
… A spoken dialogue system (SDS) allows human-computer interaction using natural … com/ 4https://www.microsoft.com/en-us/windows/cortana 5https://www.wired.com/2015/08/facebook- launches-m-new-kind-virtual-assistant/ 6https://slack.com/apps/category/At0MQP5BEF-bots …

Learning Task-Oriented Dialog with Neural Network Methods
B Liu – 2018 – bingliu.me
… 1.1 Motivation and Research Problem Dialog systems, also known as conversational agents or chatbots, are playing an increasingly … In slot-based dialog systems, the dialog … Dis- criminative models using conditional random fields [34] and recurrent neural networks [43 …

A taxonomy of attacks via the speech interface
MK Bispham, I Agrafiotis, M Goldsmith – 2018 – ora.ox.ac.uk
… Current task- based dialogue systems have some similarity with chatbots in that … Current speech dialogue systems typically use semantic representations known as semantic frames … machines, whereas slot-fitting is commonly performed using Conditional Random Fields (CRFs) …

Language-Based Bidirectional Human and Robot Interaction Learning for Mobile Service Robots
V Perera – 2018 – reports-archive.adm.cs.cmu.edu
Page 1. Language-Based Bidirectional Human and Robot Interaction Learning for Mobile Service Robots Vittorio Perera … 8 Page 25. Chapter 2 CoBot as a Service Robot This thesis introduces a novel approach to enabling bidirectional communication between users and robots …

Can we Generate Emotional Pronunciations for Expressive Speech Synthesis?
M Tahon, G Lecorvé, D Lolive – IEEE Transactions on Affective …, 2018 – ieeexplore.ieee.org
… Index Terms—Expressive speech synthesis, emotion, pronunciation adaptation, conditional random fields … is induced data obtained with scripted scenarios (for example human-robot interaction databases … while spontaneous data is better to deploy dialogue systems where the …

A scalable neural shortlisting-reranking approach for large-scale domain classification in natural language understanding
YB Kim, D Kim, JK Kim, R Sarikaya – arXiv preprint arXiv:1804.08064, 2018 – arxiv.org
… Basili et al. (2013) showed that reranking multiple ASR candidates by analyzing their syntactic prop- erties can improve spoken command understand- ing in human-robot interaction, but with more focus on ASR improvement …

A Review of Computational Approaches for Human Behavior Detection
S Nigam, R Singh, AK Misra – Archives of Computational Methods in …, 2018 – Springer
… An intelligent security robot system, NCCU Security Warrior [98], is developed for real time human behavior detection … On the other hand, variants of HMM like Maximum Entropy Markov Model (MEMM) and Conditional Random Field (CRF) do not solely depend on observations …

Early Turn-Taking Prediction for Human Robot Collaboration
T Zhou – 2018 – search.proquest.com
… CH Chattering CV Cross Validation CRF Conditional Random Field CCA Concurrent Activity DST Dempster Shafer Theory … Page 19. xviii GMM Gaussian Mixture Models HRI Human Robot Interaction HRC Human Robot Collaboration HMM Hidden Markov Models …

The history began from alexnet: A comprehensive survey on deep learning approaches
MZ Alom, TM Taha, C Yakopcic, S Westberg… – arXiv preprint arXiv …, 2018 – arxiv.org
… results show state- of-the-art performance of deep learning over traditional machine learning approaches in the field of Image Processing, Computer Vision, Speech Recognition, Machine Translation, Art, Medical imaging, Medical information processing, Robotics and control …

Safety First: Conversational Agents for Health Care
T Bickmore, H Trinh, R Asadi, S Olafsson – Studies in Conversational UX …, 2018 – Springer
… 3.2 Patient-Facing Health Dialogue Systems … 2016). Open image in new window Fig. 3.1. Fig. 3.1 Relational Agent for Palliative Care Counseling. 3.3 Special Considerations when Designing Health Counseling Dialogue Systems …

An Improved Formula for Jacobi Rotations
CF Borges, Z Majdisova, V Skala, A Monszpart… – arXiv preprint arXiv …, 2018 – arxiv.org
… Comments: Accepted in the IEEE 15th Conference on Computer and Robot Vision … Subjects: Robotics (cs.RO). arXiv:1806.08009 [pdf, other] Title: Injecting Relational Structural Representation in Neural Networks for Question Similarity …

Article in Press IJIMAI journal
AM Sandoval, J Díaz, LC Llanos, T Redondo – researchgate.net
… Biomedical entity recognition is being enhanced through Recurrent Neural Network (RNN) models, namely Long- Short-Term Memory networks [29] and hybrid architectures combining Conditional Random Fields (CRFs) [30], attention mechanisms and language modelling [31 …

Automating the anonymisation of textual corpora
L García Sardiña – 2018 – addi.ehu.eus
Page 1. Automating the Anonymisation of Textual Corpora Author: Laura Garc´?a Sardi˜na Advisors: Arantza del Pozo and Izaskun Aldezabal hap/lap Hizkuntzaren Azterketa eta Prozesamendua Language Analysis and Processing Final Thesis September 2018 …

Web forum retrieval and text analytics: A survey
D Hoogeveen, L Wang, T Baldwin… – … and Trends® in …, 2018 – nowpublishers.com
… contributions are discouraged. Having a system where people need to sign up before they can participate has the added benefit of making it difficult for bots to post spam, and it allows for personalisation of the forum. Some forums …

Speech to speech interaction system using Multimedia Tools and Partially Observable Markov Decision Process for visually impaired students
S Lokesh, B Kanisha, S Nalini, MR Devi… – Multimedia Tools and …, 2018 – Springer
… have used a range Fig. 1 Classification of dialogue systems Multimed Tools Appl Page 4 … 3 DIALOGS: component for human-robot communication SDS M21 [30] 4 GeoDialogue: CA for a Geographical information system AS M2 [54] 5 PARLANCE: DS for collaborative pursuit …

Natural Language Understanding for Healthcare Queries
V Raghuram – 2018 – eecs.berkeley.edu
… an utterance a challenge for a computer system. This imposes significant limitations on the capabilities of information retrieval systems (and dialogue systems, Q&A systems, etc.). We attempt to address this problem by adding …

Learning Representations of Text through Language and Discourse Modeling: From Characters to Sentences
Y Jernite – 2018 – search.proquest.com
Page 1. Learning Representations of Text through Language and Discourse Modeling: From Characters to Sentences by Yacine Jernite A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Computer Science …

Attentive Speaking. From Listener Feedback to Interactive Adaptation
H Buschmeier – 2018 – pub.uni-bielefeld.de
… Timo Baumann, David Schlangen, Casey Kennington, and Spyros Kousidis from Bielefeld’s Dialogue Systems group for joint work on incremental and adaptive output gener- ation in dynamic situations. ? Zofia Malisz, Petra Wagner, and Joanna Skubisz for xiii Page 16 …

Learning Activities From Human Demonstration Videos
J Lee – 2018 – scholarworks.iu.edu
… ”Please pass me the trivet” Figure 2.1: Robot learning from demonstration (LfD) enables robots to automatically learn a new task from observations. Ideally, end-users can program robots for a new task without any background of robotics technology and programming …

Deep Reinforcement Learning for Interactive Narrative Planning.
P Wang – 2018 – repository.lib.ncsu.edu
… Figure 2.1: Plot graph of the interactive fiction, Anchorhead …..15 Figure 2.2: Architecture of a spoken dialogue system with simulated users …..26 … some other fields, like spoken dialogue system, in which statistical user simulation serves an …

Dynamic Search Models and Applications
J Luo – 2018 – repository.library.georgetown.edu
Page 1. Dynamic Search Models and Applications A Dissertation submitted to the Faculty of the Graduate School of Arts and Sciences of Georgetown University in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Science By Jiyun Luo, MS …

Literature Survey and Datasets
S Poria, A Hussain, E Cambria – Multimodal Sentiment Analysis, 2018 – Springer
In this chapter we present the literature on unimodal and multimodal approaches to sentiment analysis and emotion recognition. As discussed in the Sect. 2.1, both of these topics can be brought…

In-Vehicle Voice Interface with Improved Utterance Classification Accuracy Using Off-the-Shelf Cloud Speech Recognizer
T Homma, Y Obuchi, K Shima, R Ikeshita… – … on Information and …, 2018 – search.ieice.org
Page 1. IEICE TRANS. INF. & SYST., VOL.E101–D, NO.12 DECEMBER 2018 3123 PAPER In-Vehicle Voice Interface with Improved Utterance Classification Accuracy Using Off-the-Shelf Cloud Speech Recognizer ? Takeshi …

Real-time Face Detection and Recognition Based on Deep Learning
H Wang – 2018 – aut.researchgateway.ac.nz
Page 1. Real-Time Face Detection and Recognition Based on Deep Learning Hui Wang A thesis submitted to Auckland University of Technology in partial fulfillment of the requirements for the degree of Master of Computer and Information Sciences (MCIS) …

Designing Deep Architectures for Visual Understanding
T Mordan – 2018 – tel.archives-ouvertes.fr
… AI Artificial Intelligence ANN Artificial Neural Network BoW Bag-of-Words ConvNet Convolutional Neural Network CRF Conditional Random Field CV Computer Vision DL Deep Learning DNN Deep Neural Network DP-FCN Deformable Part-based Fully Convolutional Network …

Natural Language Data Management and Interfaces
Y Li, D Rafiei – Synthesis Lectures on Data Management, 2018 – morganclaypool.com
… Second, the success of IBM’s Watson [Ferrucci, 2012] at Jeopardy and the emergence of natural language dialog systems such as Apple’s Siri, Google’s Home, Ama- zon’s Alexa, and Microsoft’s Cortana has further ignited the interest in natural language data analysis and …

Tackling Sequence to Sequence Mapping Problems with Neural Networks
L Yu – arXiv preprint arXiv:1810.10802, 2018 – arxiv.org
… We next attempt the generation tasks of seq2seq mapping with neural networks. Here (Chapter 4), we present a novel neural transduction model that aims to address the bot- tleneck of the vanilla encoder-decoders (Sutskever et al., 2014; Kalchbrenner and Blun …

Generating Animated Videos of Human Activities from Natural Language Descriptions
AS Lin, L Wu, R Corona, K Tai, Q Huang, RJ Mooney – Learning, 2018 – cs.utexas.edu
… Natural language understanding and dialog management are two integral components of interactive dialog systems … This article introduces a novel, custom-designed multi-robot platform for research on AI, robotics, and especially human–robot interaction for service robots …

Representation learning for natural language
O Mogren – 2018 – mogren.one
… humans (Turing 1950). This was published long before machines were anywhere near being able to succeed at this, while substantial progress has been made in recent years using dialog systems trained on large corpora. Most of …

Contextual Recurrent Level Set Networks and Recurrent Residual Networks for Semantic Labeling
NTH Le – 2018 – search.proquest.com
… CLS Classic Level Set. CV Chan-Vese. CNNs Convolutional Neural Networks. CRF Conditional Random Fields. CRLS Context Recurrent Level Set. CRRN Contextual Recurrent Residual Networks. FCNs Fully Convolutional Networks. GAC Geodesic Active Contours …