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Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders


While recent neural encoder-decoder models have shown great promise in modeling open-domain conversations, they often generate dull and generic responses. Unlike past work that has focused on diversifying the output of the decoder at word-level to alleviate this problem, we present a novel framework based on conditional variational autoencoders that captures the discourse-level diversity in the encoder. Our model uses latent variables to learn a distribution over potential conversational intents and generates diverse responses using only greedy decoders. We have further developed a novel variant that is integrated with linguistic prior knowledge for better performance. Finally, the training procedure is improved by introducing a bag-of-word loss. Our proposed models have been validated to generate significantly more diverse responses than baseline approaches and exhibit competence in discourse-level decision-making.



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# Title Author Topic Medium Score
1 Recent Advances in Document Summarization Jin-ge Yao, Xiaojun Wan, Jianguo Xiao 421 survey 213.31
2 Neural Information Retrieval: At the End of the Early Years Kezban Dilek Onal, Ye Zhang, Ismail Sengor Altingovde, Md Mustafizur Rahman, Pinar Karagoz, Alex Braylan, Brandon Dang, Heng-Lu Chang, Henna Kim, Quinten McNamara, Aaron Angert, Edward Banner, Vivek K 713 resource 198.24
3 PyTorch-GAN Jun-Yan Zhu, Richard Zhang, Deepak Pathak, Trevor Darrell, Alexei A. Efros, Oliver Wang, Eli Shechtman 731 library 184.37
4 The 7 NLP Techniques That Will Change How You Communicate in the Future (Part I) James Le 112 resource 174.02
5 Codra: A Novel Discriminative Framework for Rhetorical Analysis Shafiq Joty, Giuseppe Carenini, Raymond T. Ng 999 paper 168.36
7 Summaries and notes on Deep Learning research papers Denny Britz 713 resource 158.29
8 A survey of transfer learning Karl Weiss, Taghi M. Khoshgoftaar and DingDing Wang 978 resource 153.06
9 A Comparative Analysis of ChatBots APIs Author Unknown 921 resource 152.14
10 Discriminative Syntax-based Word Ordering for Text Generation Yue Zhang, Stephen Clark 999 paper 151.72
11 Deep Learning Summer School Invited Speakers with slides CIFAR 713 resource 149.18
12 Chatsbots with Machine Learning: Building Neural Conversational Agents Dmitry Persiyanov 999 resource 148.16
13 Using Artificial Intelligence to Augment Human Intelligence Shan Carter, Michael Nielsen 811 resource 147.54
14 DEEP LEARNING FOR CHATBOTS, PART 1 - INTRODUCTION Denny Britz 445 tutorial 145.36
15 The Current Best of Universal Word Embeddings and Sentence Embeddings Thomas Wolf 721 resource 144.10
16 Neural Text Embeddings for IR Bhaskar Mitra, Nick Craswell 721 tutorial 144.05
17 Tombones Computer Vision Blog Tomasz Malisiewicz 958 resource 143.86
18 Diversity and network coherence as indicators of interdisciplinarity: case studies in bionanoscience Ismael Rafols, Martin Meyer 999 paper 143.28
19 Similarity-driven Semantic Role Induction via Graph Partitioning Joel Lang, Mirella Lapata 999 paper 143.08
20 Transfer Learning - Machine Learnings Next Frontier Sebastian Ruder 978 tutorial 140.20
21 Deep Learning for NLP Best Practices Sebastian Ruder 713 tutorial 139.65
22 A Gentle Introduction to Neural Networks for Machine Learning James Le 711 resource 138.36
23 A survey of cross-lingual embedding models Sebastian Ruder 721 tutorial 137.73
24 Building a FAQ Chatbot in Python – The Future of Information Searching Yogesh Kulkarni 232 resource 135.06
25 Highlights of EMNLP 2017: Exciting Datasets, Return of the Clusters, and More! Sebastian Ruder 641 resource 134.57
26 End-to-end speech Anthony Ndirango 863 resource 134.19
27 Deep Learning for NLP, advancements and trends in 2017 Javier 711 resource 133.64
28 Neural Machine Translation (seq2seq) Tutorial Thang Luong, Eugene Brevdo, Rui Zhao 753 tutorial 133.47
29 What I learned from Deep Learning Summer School 2016 Hamid Palangi 107 tutorial 132.91
30 Building Autoencoders in Keras Francois Chollet 731 tutorial 132.73
31 ACL 2017 Report Yuta Kikuchi, Sosuke Kobayashi 711 resource 132.65
32 Deconstruction with Discrete Embeddings R2RT 711 resource 132.16
33 Rohan & Lenny #3: Recurrent Neural Networks & LSTMs Rohan Kapur 741 tutorial 130.92
34 The Unreasonable Ineffectiveness of Deep Learning in NLU Suman Deb Roy 713 tutorial 130.37
35 Recurrent Neural Networks Stephen Grossberg 741 paper 129.44
36 Your tl;dr by an ai: a deep reinforced model for abstractive summarization Romain Paulus 754 tutorial 128.39
37 Your tl;dr by an Ai: a Deep Reinforced Model for Abstractive Summarization Romain Paulus 811 resource 128.39
38 Deep Learning chatbots analysis - whats the actual tech behind them? Przemyslaw 756 tutorial 128.09
39 Generating Large Images from Latent Vectors Author Unknown 631 tutorial 128.06
40 Clustering cliques for graph-based summarization of the biomedical research literature Han Zhang, Marcelo Fiszman, Dongwook Shin, Bartomiej Wilkowski, Thomas Rindflesch 999 paper 127.78
41 Written Memories: Understanding, Deriving and Extending the LSTM R2RT 742 resource 127.49
42 Negated bio-events: analysis and identification Raheel Nawaz, Paul Thompson, Sophia Ananiadou 999 paper 127.44
43 A social network's changing statistical properties and the quality of human innovation Brian Uzzi 999 paper 125.91
44 Neural text generation: How to generate text using conditional language models Neil Yager 43 resource 125.55
45 The 8 Neural Network Architectures Machine Learning Researchers Need to Learn Nand Kishor 711 resource 125.17
46 What is the difference between convolutional neural networks, restricted Boltzmann machines, and auto-encoders? ffriend 744 tutorial 124.83
47 Ideas on interpreting machine learning Patrick Hall, Wen Phan, SriSatish Ambati 134 tutorial 124.75
48 Towards data set augmentation with GANs Pedro Ferreira 713 resource 124.58
49 Taming Recurrent Neural Networks for Better Summarization Abigail See 754 tutorial 124.57
50 Machine Learning for Humans Vishal Maini, Samer Sabri 134 tutorial 124.19
51 Word embeddings in 2017: Trends and future directions Sebastian Ruder 721 resource 123.97
52 Your TL;DR by an AI: A Deep Reinforced Model for Abstractive Summarization Romain Paulus, Caiming Xiong and Richard Socher 754 resource 123.88
53 Generative Adversarial Networks (GANs): Engine and Applications Anton Karazeev 713 resource 123.47
54 An Overview of Multi-Task Learning in Deep Neural Networks Sebastian Ruder 829 tutorial 123.31
55 Text Segmentation based on Semantic Word Embeddings Alexander Alemi, Paul Ginsparg 721 library 123.23
56 Introduction to Neural Machine Translation with GPUs (part 3) Kyunghyun Cho 753 tutorial 123.03
57 Multi-Task Learning Objectives for Natural Language Processing Author Unknown 133 resource 122.96
58 Machine Translation Without the Data Harshvardhan Gupta 451 resource 122.91
59 Generative Model Chatbots Kumar Shridhar 756 tutorial 122.35
60 Advances in Semantic Textual Similarity Yinfei Yang, Chris Tar 311 resource 122.33
61 Awesome - Most Cited Deep Learning Papers Terry Taewoong Um 713 resource 122.13
62 Deep Learning Achievements Over the Past Year Eduard Tyantov 711 resource 122.00
63 The 8 Neural Network Architectures Machine Learning Researchers Need to Learn James Le' 731 resource 121.88
64 Adversarial Autoencoders (with Pytorch) Felipe N. Ducau 713 tutorial 121.87
65 State-of-the-art neural coreference resolution for chatbots Thomas Wolf 756 tutorial 121.70
66 Improving Language Understanding with Unsupervised Learning Alec Radford 581 resource 121.09
67 Minibatch Metropolis-Hastings Daniel Seita 107 tutorial 121.06
68 Gimli: open source and high-performance biomedical name recognition David Campos, Sergio Matos, Jose Oliveira 999 paper 120.88
69 Deep Learning 2: Part 1 Lesson 4 Hiromi Suenaga 711 resource 120.49
70 The Building Blocks of Interpretability Chris Olah 614 resource 120.31
71 Image Completion with Deep Learning in TensorFlow Brandon Amos 731 tutorial 120.01
72 A general framework for analysing diversity in science, technology and society Andy Stirling 999 paper 119.87
73 Improving End-to-End Models for Speech Recognition Tara Sainath 945 resource 119.40
74 The NeuroEvolution of Augmenting Topologies (NEAT) Users Page Author Unknown 999 resource 119.39
75 A miscellany of fun deep learning papers Adrian Colyer 711 resource 118.79
76 The Unreasonable Effectiveness of Recurrent Neural Networks Andrej Karpathy 741 survey 117.77
77 An Introduction to Generative Adversarial Networks (with code in TensorFlow) John Glover 731 tutorial 117.51
78 Learned in translation: contextualized word vectors Bryan McCann 452 resource 117.31
79 Gensim integration with scikit-learn and Keras Chinmaya Pancholi 713 library 117.28
80 SippyCup Unit 2: Travel queries Bill MacCartney 365 tutorial 117.24
81 Deep Generative Models Prakash Pandey 711 resource 117.07
82 Four deep learning trends from ACL 2017: Part 1 Abigail See 713 resource 116.74
83 The Neural Network Zoo Fjodor Van Veen 712 tutorial 116.25
84 Literature Mining for the Discovery of Hidden Connections between Drugs, Genes and Diseases Frijters, Raoul AND van Vugt, Marianne AND Smeets, Ruben AND van Schaik, René AND de Vlieg, Jacob AND Alkema, Wynand 999 paper 115.90
85 A Beginner's guide to Recurrent Networks and LSTMs Skymind 742 tutorial 115.66
86 Rohan #2: Artificial intelligence, ?Progress/?Time Rohan Kapur 811 tutorial 115.42
87 Deep Learning Transcends the Bag of Words Zachary Lipton 741 tutorial 115.18
88 Using TensorFlow to generate images with PixelRNNs Phillip Kuznetsov, Noah Meyer Golmant 731 tutorial 115.17
89 Lenny #2: Autoencoders and Word Embeddings Lenny Khazan 721 tutorial 115.00
90 A history of machine translation from the Cold War to deep learning Ilya Pestov 753 resource 114.54
91 Generative Adversarial Networks - History and Overview Kiran Sudhir 641 resource 114.17
92 High-Level Explanation of Variational Inference Jason Eisner 834 resource 114.03