Neural Conversation Models 2016


Notes:

  • Neural conversation model
  • Neural conversational model

Resources:

Wikipedia:

See also:

Language Modeling & Dialog Systems 2014 | Neural Language Models 2015Neural Network & Dialog Systems 2016 | Statistical Natural Language Processing


A persona-based neural conversation model
J Li, M Galley, C Brockett, GP Spithourakis… – arXiv preprint arXiv: …, 2016 – arxiv.org”
Abstract: We present persona-based models for handling the issue of speaker consistency in neural response generation. A speaker model encodes personas in distributed embeddings that capture individual characteristics such as background information and

LSTM based Conversation Models
Y Luan, Y Ji, M Ostendorf – arXiv preprint arXiv:1603.09457, 2016 – arxiv.org
… [21] Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, and Bill Dolan. A persona-based neural conversation model. arXiv preprint arXiv:1603.06155, 2016. … [29] Oriol Vinyals and Quoc Le. A Neural Conversational Model. arXiv preprint arXiv:1506.05869, 2015. …

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
… In EMNLP, 2014. [9] J. Li, M. Galley, C. Brockett, J. Gao, and B. Dolan. A diversity-promoting objective function for neural conversation models. In NAACL-HLT, 2016. [10] W. Ling, E. Grefenstette, KM Hermann, T. Kocisky, A. Senior, F. Wang, and P. Blunsom. …

Video paragraph captioning using hierarchical recurrent neural networks
H Yu, J Wang, Z Huang, Y Yang… – Proceedings of the IEEE …, 2016 – cv-foundation.org
Page 1. Video Paragraph Captioning Using Hierarchical Recurrent Neural Networks Haonan Yu1? Jiang Wang3 Zhiheng Huang2? Yi Yang3 Wei Xu3 1Purdue University 2Facebook haonanu@gmail.com zhiheng@fb.com 3Baidu Research – Institute of Deep Learning …

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
Page 1. How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation Chia-Wei Liu1?, Ryan Lowe1?, Iulian V. Serban2?, Michael Noseworthy1?, Laurent …

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
Page 1. Learning to Respond with Deep Neural Networks for Retrieval-Based Human-Computer Conversation System Rui Yan Baidu Inc. No. 10, Xibeiwang East Road, Beijing 100193, China yanrui02@baidu.com Yiping Song Baidu Inc. No. …

Exploring the limits of language modeling
R Jozefowicz, O Vinyals, M Schuster… – arXiv preprint arXiv: …, 2016 – arxiv.org
Page 1. Exploring the Limits of Language Modeling Rafal Jozefowicz RAFALJ@GOOGLE.COM Oriol Vinyals VINYALS@GOOGLE.COM Mike Schuster SCHUSTER@GOOGLE.COM Noam Shazeer NOAM@GOOGLE.COM Yonghui Wu YONGHUI@GOOGLE.COM Google Brain …

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 …

A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues
IV Serban, A Sordoni, R Lowe, L Charlin… – arXiv preprint arXiv: …, 2016 – arxiv.org
… Auto-encoding variational bayes. In ICLR. [15] Li, J., Galley, M., Brockett, C., Gao, J., and Dolan, B. (2016). A diversity-promoting objective function for neural conversation models. In NAACL. [16] Liu, C.-W., Lowe, R., Serban, IV, Noseworthy, M., Charlin, L., and Pineau, J. (2016). …

Deep reinforcement learning for dialogue generation
J Li, W Monroe, A Ritter, M Galley, J Gao… – arXiv preprint arXiv: …, 2016 – arxiv.org
Page 1. Deep Reinforcement Learning for Dialogue Generation Jiwei Li1, Will Monroe1, Alan Ritter2, Michel Galley3, Jianfeng Gao3 and Dan Jurafsky1 1Stanford University, Stanford, CA, USA 2Ohio State University, OH, USA …

Learning end-to-end goal-oriented dialog
A Bordes, J Weston – arXiv preprint arXiv:1605.07683, 2016 – arxiv.org
… End-to-end memory networks. Proceedings of NIPS. Vinyals, O. and Le, Q. (2015). A neural conversational model. arXiv preprint arXiv:1506.05869. Wang, H., Lu, Z., Li, H., and Chen, E. (2013). A dataset for research on short-text conversations. In EMNLP. …

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
… arXiv preprint arXiv:1506.02078. [Li et al.2016a] Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, and Bill Dolan. 2016a. A diversity- promoting objective function for neural conversation models. … 2015. A neural conversational model. arXiv preprint arXiv:1506.05869. …

Neural net models for open-domain discourse coherence
J Li, D Jurafsky – arXiv preprint arXiv:1606.01545, 2016 – arxiv.org
Page 1. Neural Net Models for Open-Domain Discourse Coherence Jiwei Li and Dan Jurafsky Computer Science Department Stanford University, Stanford, CA, 94305, USA jiweil,jurafsky@stanford.edu Abstract Discourse coherence …

Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
AK Vijayakumar, M Cogswell, RR Selvaraju… – arXiv preprint arXiv: …, 2016 – arxiv.org
Page 1. DIVERSE BEAM SEARCH: DECODING DIVERSE SOLUTIONS FROM NEURAL SEQUENCE MODELS Ashwin K Vijayakumar1, Michael Cogswell1, Ramprasath R. Selvaraju1, Qing Sun1 Stefan Lee1, David Crandall2 …

Neu-IR: The SIGIR 2016 workshop on neural information retrieval
N Craswell, WB Croft, J Guo, B Mitra… – Proceedings of the 39th …, 2016 – dl.acm.org
… In Proc. SIGIR, pages 373–382. ACM, 2015. [11] O. Vinyals and Q. Le. A neural conversational model. arXiv preprint arXiv:1506.05869, 2015. [12] O. Vinyals, A. Toshev, S. Bengio, and D. Erhan. Show and tell: A neural image caption generator. …

Multi-domain joint semantic frame parsing using bi-directional RNN-LSTM
D Hakkani-Tür, G Tur, A Celikyilmaz… – Proceedings of The …, 2016 – csie.ntu.edu.tw
… Cortes, ND Lawrence, and KQ Weinberger, Eds., 2014, pp. 3104–3112. [35] O. Vinyals and QV Le, “A neural conversational model,” in ICML Deep Learning Workshop, 2015. [36] J. Duchi, E. Hazan, and Y. Singer, “Adaptive …

Smart reply: Automated response suggestion for email
A Kannan, K Kurach, S Ravi, T Kaufmann… – arXiv preprint arXiv: …, 2016 – arxiv.org
… work, develop fully generative models. Our approach is most similar to the Neural Conversation Model [24], which uses sequence-to-sequence learning to model tech support chats and movie subtitles. The primary difference of …

An online sequence-to-sequence model using partial conditioning
N Jaitly, QV Le, O Vinyals, I Sutskever… – Advances in Neural …, 2016 – papers.nips.cc
… Grammar as a foreign language. In NIPS, 2015. [19] Oriol Vinyals and Quoc V. Le. A neural conversational model. In ICML Deep Learning Workshop, 2015. [20] Oriol Vinyals, Alexander Toshev, Samy Bengio, and Dumitru Erhan. …

A neural knowledge language model
S Ahn, H Choi, T Pärnamaa, Y Bengio – arXiv preprint arXiv:1608.00318, 2016 – arxiv.org
Page 1. A Neural Knowledge Language Model Sungjin Ahn1?, Heeyoul Choi1,2, Tanel Pärnamaa1, and Yoshua Bengio1,3 1Université de Montréal, 2Samsung Electronics, 3CIFAR Senior Fellow ? ahnsungj@umontreal.ca Abstract …

Topic Augmented Neural Response Generation with a Joint Attention Mechanism
C Xing, W Wu, Y Wu, J Liu, Y Huang… – arXiv preprint arXiv …, 2016 – pdfs.semanticscholar.org
… 2015. A diversity- promoting objective function for neural conversation models. arXiv preprint arXiv:1510.03055. … 2016. A persona-based neural conversation model. [Mikolovetal.2010] Tomas Mikolov, Martin Karafiát, Lukas Burget, Jan Cernock`y, and Sanjeev Khudan- pur. …

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
Page 1. arXiv:1605.05110v1 [cs.CL] 17 May 2016 Incorporating Loose-Structured Knowledge into LSTM with Recall Gate for Conversation Modeling Zhen Xu1, Bingquan Liu1, Baoxun Wang2, Chengjie Sun1, Xiaolong Wang1 …

DeepMath-Deep Sequence Models for Premise Selection
AA Alemi, F Chollet, G Irving, C Szegedy… – arXiv preprint arXiv: …, 2016 – papers.nips.cc
… Springer, 2013. [36] O. Vinyals and Q. Le. A neural conversational model. arXiv preprint arXiv:1506.05869, 2015. [37] M. Wenzel, LC Paulson, and T. Nipkow. The Isabelle framework. In Mohamed et al. [29], pages 33–38. [38] W. Zaremba and I. Sutskever. Learning to execute. …

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 Tsung-Hsien Wen, Milica Gašic, Nikola Mrkšic, Lina M. Rojas-Barahona, Pei-Hao Su, Stefan Ultes, David Vandyke, Steve Young Cambridge …

Deep learning for information retrieval
H Li, Z Lu – Proceedings of the 39th International ACM SIGIR …, 2016 – dl.acm.org
… Grammar as a foreign language. arXiv:1412.7449, 2014. [35] O. Vinyals and QV Le. A neural conversational model. arXiv:1506.05869, 2015. [36] O. Vinyals, A. Toshev, S. Bengio, and D. Erhan. Show and tell: a neural image caption generator. arXiv:1411.4555, 2014. …

Recurrent instance segmentation
B Romera-Paredes, PHS Torr – European Conference on Computer Vision, 2016 – Springer
… neural networks. In: Advances in Neural Information Processing Systems (NIPS), pp. 545–552 (2009). 21. Vinyals, O., Le, Q.: A neural conversational model. arXiv preprint arXiv:?1506.?05869 (2015). 22. Gregor, K., Danihelka …

Conversational Contextual Cues: The Case of Personalization and History for Response Ranking
R Al-Rfou, M Pickett, J Snaider, Y Sung… – arXiv preprint arXiv: …, 2016 – arxiv.org
Page 1. Conversational Contextual Cues: The Case of Personalization and History for Response Ranking Rami Al-Rfou and Marc Pickett and Javier Snaider and Yun-hsuan Sung and Brian Strope and Ray Kurzweil Google …

Visual storytelling
THK Huang, F Ferraro, N Mostafazadeh, I Misra… – 2016 – microsoft.com
… annotations. Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, and Bill Dolan. 2016. A diversity-promoting objec- tive function for neural conversation models. NAACL HLT 2016. Chin-Yew Lin and Franz Josef Och. 2004. …

Continuously learning neural dialogue management
PH Su, M Gasic, N Mrksic, L Rojas-Barahona… – arXiv preprint arXiv: …, 2016 – arxiv.org
… In ASRU. [Vinyals and Le2015] Oriol Vinyals and Quoc Le. 2015. A neural conversational model. arXiv preprint arXiv:1506.05869. [Wen et al.2015] Tsung-Hsien Wen, Milica Gašic, Nikola Mrkšic, Pei-Hao Su, David Vandyke, and Steve Young. 2015. …

“The dialogue breakdown detection challenge: Task description, datasets, and evaluation metrics”
R Higashinaka, K Funakoshi, K Yuka… – of the Language …, 2016 – lrec-conf.org
… In JSAI Technical Report (SIG-SLUD-75-B502), pages 37–40. (in Japanese). Vinyals, O. and Le, Q. (2015). A neural conversational model. In Proc. ICML Deep Learning Workshop. Williams, J., Raux, A., Ramachandran, D., and Black, A. (2013). …

Visual storytelling
F Ferraro, N Mostafazadeh, I Misra, A Agrawal… – arXiv preprint arXiv: …, 2016 – arxiv.org
… [Li et al.2016] Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, and Bill Dolan. 2016. A diversity- promoting objective function for neural conversation models. NAACL HLT 2016. [Lin and Och2004] Chin-Yew Lin and Franz Josef Och. 2004. …

Neural utterance ranking model for conversational dialogue systems
M Inaba, K Takahashi – 17th Annual Meeting of the Special Interest …, 2016 – aclweb.org
… Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, and Bill Dolan. 2016. A diversity-promoting objec- tive function for neural conversation models. Pro- ceedings of the NAACL-HLT 2016. RD Luce. 1959. Individual choice behavior: A theo- retical analysis. Wiley, New York. …

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 …

Chatbot evaluation and database expansion via crowdsourcing
Z Yu, Z Xu, A Black, A Rudnicky – Proc. of the chatbot …, 2016 – pdfs.semanticscholar.org
… 1, pages 173–180. Association for Computational Linguistics. Vinyals, O. and Le, Q. (2015). A neural conversational model. ICML Deep Learning Workshop 2015. Yu, Z., Papangelis, A., and Rudnicky, A. (2015). Tick- Tock: A …

Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation
IV Serban, T Klinger, G Tesauro… – arXiv preprint arXiv: …, 2016 – arxiv.org
… ICML. [16] Li, J., Galley, M., Brockett, C., Gao, J., and Dolan, B. (2016). A diversity-promoting objective function for neural conversation models. In NAACL. [17] Liu, C.-W., Lowe, R., Serban, IV, Noseworthy, M., Charlin, L., and Pineau, J. (2016). …

CFGs-2-NLU: Sequence-to-sequence learning for mapping utterances to semantics and pragmatics
AJ Summerville, J Ryan, M Mateas… – arXiv preprint arXiv: …, 2016 – arxiv.org
… Vinyals, O., Kaiser, L., Koo, T., Petrov, S., Sutskever, I., Hinton, G.: Grammar as a foreign language. In: Advances in Neural Information Processing Systems (2015) 20. Vinyals, O., Le, Q.: A neural conversational model. arXiv preprint arXiv:1506.05869 (2015) 21. …

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
… 1494–1504. Vinyals, Q., Le, QL (2015). A Neural Conversation Model. arXiv preprint arXiv:1506.05869 Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhutdinov, R., Zemel, R., Bengio, Y. (2015). Show, Attend and Tell: Neural Image Caption Generation with Visual Attention. …

Emulating human conversations using convolutional neural network-based ir
A Prakash, C Brockett, P Agrawal – arXiv preprint arXiv:1606.07056, 2016 – arxiv.org
… In Interactive Storytelling, pages 32–40. Springer, 2008. [16] O. Vinyals and Q. Le. A neural conversational model. arXiv preprint arXiv:1506.05869, 2015. [17] W.-t. Yih, X. He, and C. Meek. Semantic parsing for single-relation question answering. In ACL, pages 643–648, 2014. …

Generalizing and Hybridizing Count-based and Neural Language Models
G Neubig, C Dyer – arXiv preprint arXiv:1606.00499, 2016 – arxiv.org
Page 1. Generalizing and Hybridizing Count-based and Neural Language Models Graham Neubig† and Chris Dyer‡ †Carnegie Mellon University, USA ‡Google DeepMind, United Kingdom Abstract Language models (LMs …

“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 …

Attention and Augmented Recurrent Neural Networks
C Olah, S Carter – Distill, 2016 – distill.pub”

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 …

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 …

LSTM-based Mixture-of-Experts for Knowledge-Aware Dialogues
P Le, M Dymetman, JM Renders – arXiv preprint arXiv:1605.01652, 2016 – arxiv.org
… In Advances in neu- ral information processing systems, pages 3104– 3112. [VinyalsandLe2015] Oriol Vinyals and Quoc Le. 2015. A neural conversational model. arXiv preprint arXiv:1506.05869. [Yihetal.2015] Wen-tau Yih, Ming-Wei Chang, Xi- aodong He, and Jianfeng Gao. …

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
… [8] Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, and Bill Dolan. A diversity-promoting objective function for neural conversation models. In NAACL-HLT, pages 110–119, 2016. … 10 Page 11. [22] Oriol Vinyals and Quoc Le. A neural conversational model. …

On the Evaluation of Dialogue Systems with Next Utterance Classification
R Lowe, IV Serban, M Noseworthy, L Charlin… – arXiv preprint arXiv: …, 2016 – arxiv.org
… In Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT 2015). O. Vinyals and Q. Le. 2015. A neural conversational model. ICML Deep Learning Workshop. MA Walker, DJ Litman, CA Kamm, and A. Abella. 1997. …

Learning to Start for Sequence to Sequence Architecture
Q Zhu, W Zhang, L Zhou, T Liu – arXiv preprint arXiv:1608.05554, 2016 – arxiv.org
… [Li et al.2015] Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, and Bill Dolan. 2015. A diversity-promoting objective function for neural conversation models. arXiv preprint arXiv:1510.03055. … 2015. A neural conversational model. arXiv preprint arXiv:1506.05869.

Natural language processing
GTD Parsing – Proceedings of the ACL Workshop on Statistical …, 2016 – research.google.com
… Intelligent User Interfaces 2017, ACM, Limassol, Cyprus (to appear). Generating Long and Diverse Responses with Neural Conversation Models. Louis Shao, Stephan Gouws, Denny Britz, Anna Goldie, Brian Strope, Ray Kurzweil. arXiv (2017). German Typographers vs. …

Dynamical Integration of Language and Behavior in a Recurrent Neural Network for Human–Robot Interaction
T Yamada, S Murata, H Arie, T Ogata – Frontiers in Neurorobotics, 2016 – ncbi.nlm.nih.gov”

Multi-agent cooperation and the emergence of (natural) language
A Lazaridou, A Peysakhovich, M Baroni – arXiv preprint arXiv:1612.07182, 2016 – arxiv.org
Page 1. Under review as a conference paper at ICLR 2017 MULTI-AGENT COOPERATION AND THE EMERGENCE OF (NATURAL) LANGUAGE Angeliki Lazaridou1,2, Alexander Peysakhovich1, Marco Baroni2 1Facebook …

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 Su, Milica Gašic, Nikola Mrkšic, Lina Rojas-Barahona, Stefan Ultes, David Vandyke, Tsung-Hsien Wen and Steve Young …

A Neural Conversational Model for Chatbots
S Agrawal, MM Haidri – IJRCCT, 2016 – ijrcct.org
Abstract One of the fundamental objectives of Computer Science is to reduce the menial, repetitive and mundane tasks. It might be arithmetic calculations or maintaining huge amount of data. With the advent of Artificial Intelligence and Machine Learning, we are a

An Attentional Neural Conversation Model with Improved Specificity
K Yao, B Peng, G Zweig, KF Wong – arXiv preprint arXiv:1606.01292, 2016 – arxiv.org
Abstract: In this paper we propose a neural conversation model for conducting dialogues. We demonstrate the use of this model to generate help desk responses, where users are asking questions about PC applications. Our model is distinguished by two characteristics.

Generative Deep Neural Networks for Dialogue: A Short Review
IV Serban, R Lowe, L Charlin, J Pineau – arXiv preprint arXiv:1611.06216, 2016 – arxiv.org
… J. Li, M. Galley, C. Brockett, J. Gao, and B. Dolan. A diversity-promoting objective function for neural conversation models. In NAACL, 2016a. J. Li, W. Monroe, A. Ritter, and D. Jurafsky. … O. Vinyals and Q. Le. A neural conversational model. ICML, Workshop, 2015. …

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
… [Li et al., 2016a] Jiwei Li, M. Galley, C. Brockett, J. Gao, and B. Dolan. A diversity-promoting objective function for neural conversation models. In NAACL, 2016. … [Vinyals and Le, 2015] Oriol Vinyals and Quoc Le. A neural conversational model. …

Topic Aware Neural Response Generation
C Xing, W Wu, Y Wu, J Liu, Y Huang, M Zhou… – arXiv preprint arXiv: …, 2016 – arxiv.org
Page 1. Topic Aware Neural Response Generation Chen Xing1 2 , Wei Wu4 , Yu Wu3 , Jie Liu1 2 , Yalou Huang1 2 , Ming Zhou4 , Wei-Ying Ma4 1College of Computer and Control Engineering, Nankai University, Tianjin, China …

Towards an end to end Dynamic Dialogue System
V Bhalla – researchgate.net
“… [2]. The neural conversational model proposed by Vinayls & Le [3] is an end to end purely data-driven approach to the problem which lacks domain knowledge that however, does not capture a consistent personality with its short conversational answers. …

Personified Autoresponder
A Mahendra – cs224d.stanford.edu
“… Unlike the neural conversational model proposed by Vinyals, the persona based conversational model proposed by Li et al captures author specific characteristics. Li et al. … [VL15] Oriol Vinyals and Quoc V. Le. “A Neural Conversational Model”. In: CoRR abs/1506.05869 (2015). …

Batch Policy Gradient Methods for Improving Seq2Seq Conversation Models
K Kandasamy, Y Bachrach, R Tomioka, D Tarlow… – 2016 – ml.cmu.edu
Page 1. Data Analysis Project Machine Learning Department, Carnegie Mellon University BATCH POLICY GRADIENT METHODS FOR IMPROVING SEQ2SEQ CONVERSATION MODELS Kirthevasan Kandasamy kandasamy@cs.cmu.edu November 2016 ABSTRACT …

Neural Discourse Modeling of Conversations
JM Pierre, M Butler, J Portnoff, L Aguilar – arXiv preprint arXiv:1607.04576, 2016 – arxiv.org
… Since discourse analysis considers language at the conversation-level, including its social and psycholog- ical context, it is a useful framework for guiding the extension of end-to-end neural conversational models. Drawing on concepts from discourse analysis such as …

Sequence-to-Sequence Learning for End-to-End Dialogue Systems
J Van Landeghem – 2016 – researchgate.net
“… Data Page 16. LSTM encoder-decoder architecture Encoder-Decoder model A Neural Conversation Model (Torch) https://github.com/macournoyer/neuralconvo … [2] A Neural Conversational Model [3] A Diversity-Promoting Objective Function for Neural Conversation …

Learning Through Dialogue Interactions
J Li, AH Miller, S Chopra, MA Ranzato… – arXiv preprint arXiv: …, 2016 – arxiv.org
Page 1. Under review as a conference paper at ICLR 2017 LEARNING THROUGH DIALOGUE INTERACTIONS Jiwei Li, Alexander H. Miller, Sumit Chopra, Marc’Aurelio Ranzato, Jason Weston Facebook AI Research, New York, USA {jiwel,ahm,spchopra,ranzato,jase}@fb.com …

Report on the SIGIR 2016 Workshop on Neural Information Retrieval (Neu-IR)
N Craswell, WB Croft, J Guo, B Mitra, M de Rijke – pdfs.semanticscholar.org
… In Proc. WWW, pages 1069–1079. ACM, April 2016. [37] O. Vinyals and Q. Le. A neural conversational model. arXiv preprint arXiv:1506.05869, 2015. [38] O. Vinyals, A. Toshev, S. Bengio, and D. Erhan. Show and tell: A neural image caption generator. …

Reading Comprehension using Entity-based Memory Network
X Wang, K Sudoh, M Nagata, T Shibata… – arXiv preprint arXiv: …, 2016 – arxiv.org
… Lawrence Erlbaum Associates (1978) 11. Li, J., Galley, M., Brockett, C., Gao, J., Dolan, B.: A diversity-promoting objective function for neural conversation models. … Li, J., Galley, M., Brockett, C., Gao, J., Dolan, B.: A persona-based neural conversation model. …

Privacy Protection for Natural Language: Neural Generative Models for Synthetic Text Data
AG Ororbia II, F Linder, J Snoke – arXiv preprint arXiv:1606.01151, 2016 – researchgate.net
… [11] LE, QV, AND MIKOLOV, T. Distributed representations of sentences and documents. [12] LI, J., GALLEY, M., BROCKETT, C., GAO, J., AND DOLAN, B. A persona-based neural conversation model. … 8 Page 9. [26] VINYALS, O., AND LE, Q. A neural conversational model. …

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
Page 1. Sequential Match Network: A New Architecture for Multi-turn Response Selection in Retrieval-based Chatbots Yu Wu†? , Wei Wu‡ , Zhoujun Li† , Ming Zhou‡ †State Key Lab of Software Development Environment, Beihang …

Privacy Protection for Natural Language Records: Neural Generative Models for Releasing Synthetic Twitter Data
II Ororbia, G Alexander, F Linder, J Snoke – arXiv preprint arXiv: …, 2016 – arxiv.org
Page 1. Privacy Protection for Natural Language Records: Neural Generative Models for Releasing Synthetic Twitter Data Alexander G. Ororbia II Fridolin Linder Joshua Snoke Information Science & Technology The Pennsylvania …

Continuous-Space Language Processing: Beyond Word Embeddings
M Ostendorf – International Conference on Statistical Language and …, 2016 – Springer
… ACL), pp. 211–225 (2015). 41. Li, J., Galley, M., Brockett, C., Gao, J., Dolan, B.: A persona-based neural conversation model. In: Proceedings of the Annual Meeting Association for Computational Linguistics (ACL) (2016). 42. Li …

Deep Learning
G Castellucci – art.uniroma2.it
“Page 1. Introduction to Neural Networks and Deep Learning Giuseppe Castellucci Web Mining & Retrieval 2015/2016 18/04/2016 Page 2. Outline ? Introduction ? Why “Deep” Learning? ? From Perceptron to Neural Networks ? Cost Function ? Gradient Descent …

Neural Network Approaches to Dialog Response Retrieval and Generation
NIO Lasguido, S Sakti, G Neubig… – … on Information and …, 2016 – search.ieice.org
Page 1. 2508 IEICE TRANS. INF. & SYST., VOL.E99–D, NO.10 OCTOBER 2016 PAPER Special Section on Recent Advances in Machine Learning for Spoken Language Processing Neural Network Approaches to Dialog Response Retrieval and Generation …

DialPort: Connecting the Spoken Dialog Research Community to Real User Data
T Zhao, K Lee, M Eskenazi – arXiv preprint arXiv:1606.02562, 2016 – arxiv.org
… Journal of artificial intelli- gence research, 37(1):141–188. [VinyalsandLe2015] Oriol Vinyals and Quoc Le. 2015. A neural conversational model. arXiv preprint arXiv:1506.05869. [Wallace and others2005] Richard Wallace et al. 2005. …

Ranking Responses Oriented to Conversational Relevance in Chat-bots
B Wu, B Wang, H Xue – aclweb.org
… 2014. Sequence to sequence learning with neural networks. In Advances in neural information processing systems, pages 3104–3112. Oriol Vinyals and Quoc Le. 2015. A neural conversational model. arXiv preprint arXiv:1506.05869. Joseph Weizenbaum. 1966. …

RNN-based Encoder-decoder Approach with Word Frequency Estimation
J Suzuki, M Nagata – arXiv preprint arXiv:1701.00138, 2016 – arxiv.org
… URL http://jmlr.org/proceedings/papers/v37/xuc15.html. Oriol Vinyals and Quoc V. Le. A Neural Conversational Model. CoRR, abs/1506.05869, 2015. URL http://arxiv.org/ abs/1506.05869. Lifeng Shang, Zhengdong Lu, and Hang Li. …

Backchanneling via Twitter Data for Conversational Dialogue Systems
M Inaba, K Takahashi – International Conference on Speech and …, 2016 – Springer
… Trans. Jpn. Soc. Artif. Intell. 27(2), 16–21 (2012)CrossRef. 17. Vinyals, O., Le, Q.: A neural conversational model. arXiv preprint arXiv:?1506.?05869 (2015). 18. Wallace, R.: The anatomy of alice. In: Epstein, R., Roberts, G., Beber, G. (eds.) Parsing the Turing Test, pp. 181–210. …

Transforming Chatbot Responses to Mimic Domain-specific Linguistic Styles
S Banerjee, P Biyani, K Tsioutsiouliklis – workshop.colips.org
… Li et. al, [9] describe a persona-based neural conversation model where, the persona is restricted to general human-like behavior and not specific persona styles. In this work, our focus is to provide an accurate answer to questions asked to the conversational agent. …

Still not there? Comparing Traditional Sequence-to-Sequence Models to Encoder-Decoder Neural Networks on Monotone String Translation Tasks
C Schnober, S Eger, ELD Dinh, I Gurevych – arXiv preprint arXiv: …, 2016 – arxiv.org
Page 1. arXiv:1610.07796v2 [cs.CL] 26 Oct 2016 Still not there? Comparing Traditional Sequence-to-Sequence Models to Encoder-Decoder Neural Networks on Monotone String Translation Tasks Carsten Schnober†‡, Steffen Eger†, Erik-Lân Do Dinh†, and Iryna Gurevych†‡ …

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
Page 1. Response Selection with Topic Clues for Retrieval-based Chatbots Yu Wu†? , Wei Wu‡ , Zhoujun Li† , Ming Zhou‡ †State Key Lab of Software Development Environment, Beihang University, Beijing, China ‡ Microsoft …

Analysis of sequence to sequence neural networks on grapheme to phoneme conversion task
S Achanta, A Pandey… – Neural Networks (IJCNN) …, 2016 – ieeexplore.ieee.org
… tion with visual attention,” arXiv preprint arXiv:1502.03044, 2015. [12] Oriol Vinyals and Quoc Le, “A neural conversational model,” arXiv preprint arXiv:1506.05869, 2015. [13] Liang Lu, Xingxing Zhang, Kyunghyun Cho, and …

Non-sentential Question Resolution using Sequence to Sequence Learning
V Kumar, S Joshi – aclweb.org
… 2014. Adam: A method for stochastic optimization. CoRR, abs/1412.6980. Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, and William B. Dolan. 2016. A persona-based neural conversation model. CoRR, abs/1603.06155. James MacQueen et al. 1967. …

An oral exam for measuring a dialog system’s capabilities
D Cohen, I Lane – Proceedings of the Thirtieth AAAI Conference on …, 2016 – dl.acm.org
… 21. Turing, AM 1950. Computing machinery and intelligence. Mind 433-460. 22. Vinyals, O., and Le, QV 2015. A neural conversational model. CoRR abs/1506.05869. 23. Voorhees, EM, and Buckland, L. 2003. Overview of the trec 2003 question answering track. In TREC, 54-68. …

Coherent Dialogue with Attention-based Language Models
H Mei, M Bansal, MR Walter – arXiv preprint arXiv:1611.06997, 2016 – arxiv.org
Page 1. Coherent Dialogue with Attention-based Language Models Hongyuan Mei Johns Hopkins University hmei@cs.jhu.edu Mohit Bansal UNC Chapel Hill mbansal@cs.unc. edu Matthew R. Walter TTI-Chicago mwalter@ttic.edu Abstract …

Learning Language Models of Movie Characters
OH Elibol, M Gholami – cs224d.stanford.edu
… Transfer Learning – Handbook of Research on Machine Learn- ing. [17] Oriol Vinyals and Quoc B. Le. A neural conversational model. [18] Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson. How transferable are features in deep neural networks. 8

A Web-based Platform for Collection of Human-Chatbot Interactions
L Lin, LF D’Haro, R Banchs – … of the Fourth International Conference on …, 2016 – dl.acm.org
… The Ubuntu Chat Corpus for Multiparticipant Chat Analysis. AAAI Spring Symposium: Analyzing Microtext. 2013. 8. Oriol Vinyals, Quoc Le. A Neural Conversational Model. 2015. 9. Richard S. Wallace, Be Your Own Botmaster. ALICE AI Foundation, 2005. 10. …

Augmented LSTM Framework to Construct Medical Self-diagnosis Android
C Liu, H Sun, N Du, S Tan, H Fei, W Fan… – Data Mining (ICDM), …, 2016 – ieeexplore.ieee.org
Page 1. Augmented LSTM Framework to Construct Medical Self-diagnosis Android Chaochun Liu ? , Huan Sun † , Nan Du ? , Shulong Tan ? , Hongliang Fei ? , Wei Fan ? , Tao Yang ‡ , Hao Wu § , Yaliang Li ¶ and Chenwei Zhang …

Compositional Sentence Representation from Character within Large Context Text
G Kim, H Lee, J Choi, S Lee – arXiv preprint arXiv:1605.00482, 2016 – arxiv.org
Page 1. arXiv:1605.00482v3 [cs.CL] 3 Jun 2016 Compositional Sentence Representation from Character within Large Context Text Geonmin Kim, Hwaran Lee, Jisu Choi, Soo-young Lee Korea Advanced Institute of Science …

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
… Recently, with the fast development of deep learning techniques, efforts are devoted in the neural network-based conversation sys- tems. A neural conversation model is proposed using a sequence- to-sequence manner [33]. …

Deep Reinforcement Learning for Multi-Domain Dialogue Systems
H Cuayáhuitl, S Yu, A Williamson, J Carse – arXiv preprint arXiv: …, 2016 – arxiv.org
… Multi-domain dialogue success classifiers for policy training. In ASRU, 2015. [21] O. Vinyals and QV Le. A neural conversational model. CoRR, abs/1506.05869, 2015. [22] Z. Wang, H. Chen, G. Wang, H. Tian, H. Wu, and H. Wang. …

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
Page 1. arXiv:1611.00483v2 [cs.CL] 3 Nov 2016 Detecting Context Dependent Messages in a Conversational Environment Chaozhuo Li†, Yu Wu†, Wei Wu‡, Chen Xing?, Zhoujun Li†, Ming Zhou‡ †State Key Lab of Software …

Quote Recommendation in Dialogue using Deep Neural Network
H Lee, Y Ahn, H Lee, S Ha, S Lee – … of the 39th International ACM SIGIR …, 2016 – dl.acm.org
… In AAAI, 2015. [11] T. Tieleman and G. Hinton. Lecture 6.5 – rmsprop, coursera: Neural networks for machine learning. In Technical report, 2012. [12] O. Vinyals and QV Le. A neural conversational model. In arXiv, 2015. [13] S. Young, M. Gasic, B. Thomson, and JD Williams. …

Challenges in Building Highly-Interactive Dialog Systems
NG Ward, D DeVault – pdfs.semanticscholar.org
… Motion fields for interactive character locomotion. Communications of the ACM 57(6):101–108. Li, J.; Galley, M.; Brockett, C.; Gao, J.; and Dolan, B. 2016. A persona-based neural conversation model. arXiv preprint arXiv:1603.06155. Li, L.; He, H.; and Williams, JD 2014. …

On Dialogue Breakdown: Annotation and Detection
K Funakoshi, R Higashinaka, M Inaba, Y Kobayashi… – workshop.colips.org
… LSTM-RNN Encoding of word frequencies by use of NCM, LSTM, bag- of-words embedding, an extended NCM baseline CRF Word frequencies (RNN:Recurrent Neural Network, LSTM:Long Short-Term Memory, DNN:Deep Neural Network, NCM:Neural Conversational Model) …

A Proposal for Evaluating Answer Distillation from Web Data
B Mitra, G Simon, J Gao, N Craswell, L Deng – plg2.cs.uwaterloo.ca
… Advantages of query biased summaries in information retrieval. In SIGIR, 1998. [18] O. Vinyals and Q. Le. A neural conversational model. arXiv preprint arXiv:1506.05869, 2015. [19] EM Voorhees and DM Tice. The trec-8 question answering track evaluation. …

USTC at NTCIR-12 STC Task
J Zhang, J Hou, S Zhang, L Dai – research.nii.ac.jp
… arXiv preprint arXiv:1503.02364, 2015. [5] Oriol Vinyals and Quoc Le. A neural conversational model. arXiv preprint arXiv:1506.05869, 2015. [6] Lifeng Shang, Tetsuya Sakai, Zhengdong Lu, Hang Li, Ryuichiro Higashinaka, and Yusuke Miyao. …

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
… However, ”A neural conversational model”[22] , ”Contextual LSTM…”[6], ”playing Atari with deep reinforcement learning”[139], ”mastering the game of Go…”[138] and numerous other already mentioned articles have recently begun to really attack this problem. …

Bootstrapping incremental dialogue systems: using linguistic knowledge to learn from minimal data
D Kalatzis, A Eshghi, O Lemon – arXiv preprint arXiv:1612.00347, 2016 – arxiv.org
… Journal of Semantics, 21(3):283–339, 2004. [11] Oriol Vinyals and Quoc Le. A neural conversational model. arXiv, (1506.05869v3), 2015. [12] Tsung-Hsien Wen, Milica Gašic, Nikola Mrkšic, Lina M. Rojas-Barahona, Pei-Hao Su, David Vandyke, and Steve Young. …

Abstractive Headline Generation for Spoken Content by Attentive Recurrent Neural Networks with ASR Error Modeling
LC Yu, H Lee, L Lee – arXiv preprint arXiv:1612.08375, 2016 – arxiv.org
… 2692–2700. [21] Oriol Vinyals and Quoc Le, “A neural conversational model,” in International Conference on Machine Learn- ing: Deep Learning Workshop, 2015. [22] David Graff and Ke Chen, “Chinese gigaword,” LDC Catalog No.: LDC2003T09, ISBN, vol. 1, pp. …

The DialPort Portal: Grouping Diverse Types of Spoken Dialog Systems
T Zhao, K Lee, M Eskenazi – workshop.colips.org
… Page 10. 10 17. Turney, PD, Pantel, P., et al.: From frequency to meaning: Vector space models of semantics. Journal of artificial intelligence research 37(1), 141–188 (2010) 18. Vinyals, O., Le, Q.: A neural conversational model. arXiv preprint arXiv:1506.05869 (2015) 19. …

Recent Advances on Human-Computer Dialogues
X Wang, C Yuan – CAAI Transactions on Intelligence Technology, 2016 – Elsevier
Human-Computer dialogue systems provide a natural language based interface between human and computers. They are widely demanded in network information services.

Review of state-of-the-arts in artificial intelligence. Present and future of AI.
V Shakirov – alpha.sinp.msu.ru
“… language. However, ”A neural conversational model”[17] , ”Contextual LSTM…”[1], 5 Page 6. … level. It’s reasonable to expect the same < 3 years gap from ”A neural conversational model” winning over Cleverbot to human-level reasoning. …

Botta: An Arabic Dialect Chatbot
DA Ali, N Habash – aclweb.org
… Nizar Habash. 2010. Introduction to Arabic Natural Language Processing. Morgan & Claypool Publishers. Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, and Bill Dolan. 2016. A persona-based neural conversation model. arXiv preprint arXiv:1603.06155. …

An Online Platform for Crowd-sourcing Data from Interactions with Chatbots
LF D’Haro, L Lue – Proceedings of WOCHAT, IVA, 2016 – researchgate.net
… Compari- son of Eliza with modern dialogue systems. Computers in Human Behavior 58, 278–295 (may 2016), http://www.sciencedirect.com/science/article/pii/ S0747563216300048 9. Vinyals, O., Le, Q.: A neural conversational model. …

Learning to Extract Conditional Knowledge for Question Answering using Dialogue
P Wang, L Ji, J Yan, L Jin, WY Ma – … of the 25th ACM International on …, 2016 – dl.acm.org
Page 1. Learning to Extract Conditional Knowledge for Question Answering using Dialogue Pengwei Wang South China University of Technology Guangzhou, China w.pengwei@mail.scut. edu.cn Lei Ji Microsoft Research Asia Beijing, China leiji@microsoft.com …

Reference-Aware Language Models
Z Yang, P Blunsom, C Dyer, W Ling – arXiv preprint arXiv:1611.01628, 2016 – arxiv.org
Page 1. Under review as a conference paper at ICLR 2017 REFERENCE-AWARE LANGUAGE MODELS Zichao Yang1?, Phil Blunsom2,3, Chris Dyer1,2, and Wang Ling2 1Carnegie Mellon University, 2DeepMind, and 3University …

Creativity in Machine Learning
M Thoma – arXiv preprint arXiv:1601.03642, 2016 – arxiv.org
… on. IEEE, 2013, pp. 1–8. [Online]. Available: http://ieeexplore.ieee.org/ xpls/abs all.jsp?arnumber=6751109 [VL15] O. Vinyals and Q. Le, “A neural conversational model,” arXiv preprint arXiv:1506.05869, Jul. 2015. [Online]. Available …

A Generative Deep Learning for Generating Korean Abbreviations
SJ Choi, AY Kim, SB Park, SY Park – Australasian Joint Conference on …, 2016 – Springer
… Sutskever, I., Vinyals, O., Le, V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, pp. 3104–3112 (2014). 14. Vinyals, O., Le, Q.: A Neural Conversational Model. arXiv preprint arXiv:1506.05869 (2015). 15. …

Multi-modal Variational Encoder-Decoders
IV Serban, II Ororbia, G Alexander, J Pineau… – arXiv preprint arXiv: …, 2016 – arxiv.org
Page 1. MULTI-MODAL VARIATIONAL ENCODER-DECODERS Iulian V. Serban†?, Alexander G. Ororbia II×?, Joelle Pineau‡, Aaron Courville† † Department of Computer Science and Operations Research, Universite de …

An Empirical Evaluation of various Deep Learning Architectures for Bi-Sequence Classification Tasks
A Laha, V Raykar – arXiv preprint arXiv:1607.04853, 2016 – arxiv.org
… In 4th International Conference for Learning Representations, Puerto Rico, 2016. Oriol Vinyals and Quoc V. Le. 2015. A neural conversational model. CoRR, abs/1506.05869. Oriol Vinyals, Alexander Toshev, Samy Bengio, and Dumitru Erhan. 2015. …

Visual Dialog
A Das, S Kottur, K Gupta, A Singh, D Yadav… – arXiv preprint arXiv: …, 2016 – arxiv.org
Page 1. Visual Dialog Abhishek Das1, Satwik Kottur2, Khushi Gupta2?, Avi Singh3?, Deshraj Yadav1, José MF Moura2, Devi Parikh4, Dhruv Batra4 1Virginia Tech, 2Carnegie Mellon University, 3UC Berkeley, 4Georgia Institute of Technology Abstract …

Unsupervised Pretraining for Sequence to Sequence Learning
P Ramachandran, PJ Liu, QV Le – arXiv preprint arXiv:1611.02683, 2016 – arxiv.org
… arXiv preprint arXiv:1604.01729, 2016. Oriol Vinyals and Quoc V. Le. A neural conversational model. arXiv preprint arXiv:1506.05869, 2015. Oriol Vinyals, Lukasz Kaiser, Terry Koo, Slav Petrov, Ilya Sutskever, and Geoffrey E. Hinton. Gram- mar as a foreign language. In NIPS. …

Sequence-level knowledge distillation
Y Kim, AM Rush – arXiv preprint arXiv:1606.07947, 2016 – arxiv.org
Page 1. Sequence-Level Knowledge Distillation Yoon Kim yoonkim@seas.harvard. edu Alexander M. Rush srush@seas.harvard.edu School of Engineering and Applied Sciences Harvard University Cambridge, MA, USA Abstract …

End-to-End Speech Recognition Models
W Chan – 2016 – williamchan.ca
“Page 1. End-to-End Speech Recognition Models Submitted in partial fullfillment of the requirements for the degree of Doctor of Philosophy in Department of Electrical and Computer Engineering William Chan BASc Computer …

Automatic Turn Segmentation for Movie & TV Subtitles
P Lison, R Meena – 2016 IEEE Workshop on Spoken Language …, 2016 – diva-portal.org
… national Conference on Language Resources and Evaluation (LREC 2016), 2016. [2] O. Vinyals and QV Le, “A neural conversational model,” CoRR, vol. abs/1506.05869, 2015. [3] V. Petukhova, R. Agerri, M. Fishel, S. Penkale …

Text Selenography Based on CIA-poetry Generation Using Markov Chain Model.
Y Luo, Y Huang, F Li, C Chang – KSII Transactions on …, 2016 – search.ebscohost.com
Page 1. KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS VOL. 10, NO. 9, Sep. 2016 4568 Copyright ?2016 KSII Text Steganography Based on Ci-poetry Generation Using Markov Chain Model Yubo Luo1 …

Natural Language Generation through Character-Based RNNs with Finite-State Prior Knowledge
R Goyal, M Dymetman, E Gaussier, U LIG – pdfs.semanticscholar.org
… Theano: A Python framework for fast computation of mathematical expres- sions. arXiv e-prints, abs/1605.02688, May. Oriol Vinyals and Quoc Le. 2015. A neural conversational model. arXiv preprint arXiv:1506.05869. Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly. 2015. …

Diverse image captioning via grouptalk
Z Wang, F Wu, W Lu, J Xiao, X Li, Z Zhang… – Proceedings of the …, 2016 – ijcai.org
Page 1. Diverse Image Captioning via GroupTalk Zhuhao Wang, Fei Wu, Weiming Lu, Jun Xiao, Xi Li, Zitong Zhang, Yueting Zhuang College of Computer Science, Zhejiang University, China {zhuhaow, wufei, luwm, junx, xilizju, ztzhang, yzhuang}@zju.edu.cn Abstract …

Applying Chatbots to the Internet of Things: Opportunities and Architectural Elements
R Kar, R Haldar – arXiv preprint arXiv:1611.03799, 2016 – arxiv.org
Page 1. Applying Chatbots to the Internet of Things: Opportunities and Architectural Elements Rohan Kar1 Hyderabad, India rohankar99@gmail.com Rishin Haldar2 School of Computing Sciences and Engineering, VIT University, Vellore, India rishinhaldar@vit.ac.in …

Machine Learning: Algorithms and Applications
M Mohammed, MB Khan, EBM Bashier – 2016 – books.google.com
Page 1. Maching Learning F. | – – º Muhammad Muhammad Badruddin Khan Eihah Bashier Mohammed Bashier gº CRC Press Taylor & Francis Group Page 2. Machine Learning Algorithms and Applications Page 3. OTHER TITLES …

Reinforcement learning with natural language signals
S Sidor – 2016 – dspace.mit.edu
“Page 1. Reinforcement Learning with Natural Language Signals by Szymon Sidor BA, University of Cambridge (2013) MASSACHUS ITUTE OF TECHNLG APR 152016 LIBRARIES ARCHIVES Submitted to the Department of Electrical Engineering and Computer Science …

Toward a debating machine: A news sentence network analysis algorithm based on similarity and cooccurrence
D PARK – Proceedings of HCI Korea, 2016 – dl.acm.org
… Agenda setting and political advertising: Origins of the news agenda. Political Communication, 1994, 11.3: 249?262. 27. VINYALS, Oriol; LE, Quoc. A neural conversational model. arXiv preprint arXiv:1506.05869, 2015. 28. http://www.kinds.or.kr/ 29. http://147.47.123.2/expert/ …

Continuous spaces in statistical machine Translation
ÁP Abril – 2016 – riunet.upv.es
“Page 1. Departamento de Sistemas Informáticos y Computación Universidad Politécnica de Valencia Continuous Spaces in Statistical Machine Translation Master Thesis Máster en Inteligencia Artificial, Reconocimiento de Formas e Imagen Digital Author: Álvaro Peris Abril …

A Base Camp for Scaling AI
CJC Burges, T Hart, Z Yang, S Cucerzan… – arXiv preprint arXiv: …, 2016 – arxiv.org
Page 1. A Base Camp for Scaling AI CJC Burges, T. Hart, Z. Yang, ? S. Cucerzan, RW White, A. Pastusiak, J. Lewis Microsoft Research December 21st, 2016 Abstract Modern statistical machine learning (SML) methods share …

Data-driven computer vision for science and the humanities
S Lee – 2016 – search.proquest.com
“Data-driven computer vision for science and the humanities. Abstract. The rate at which humanity is producing visual data from both large-scale scientific imaging and consumer photography has been greatly accelerating in the past decade. …