Open AccessPosted Content
Neural Approaches to Conversational AI
TLDR
In this article, the authors present a survey of state-of-the-art neural approaches to conversational AI, and discuss the progress that has been made and challenges still being faced, using specific systems and models as case studies.Abstract:
The present paper surveys neural approaches to conversational AI that have been developed in the last few years. We group conversational systems into three categories: (1) question answering agents, (2) task-oriented dialogue agents, and (3) chatbots. For each category, we present a review of state-of-the-art neural approaches, draw the connection between them and traditional approaches, and discuss the progress that has been made and challenges still being faced, using specific systems and models as case studies.read more
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DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation
Yizhe Zhang,Siqi Sun,Michel Galley,Yen-Chun Chen,Chris Brockett,Xiang Gao,Jianfeng Gao,Jingjing Liu,Bill Dolan +8 more
TL;DR: The authors presented a large, tunable neural conversational response generation model, DialoGPT (dialogue generative pre-trained transformer) trained on 147M conversation-like exchanges extracted from Reddit comment chains over a period spanning from 2005 through 2017.
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Towards a Human-like Open-Domain Chatbot
Daniel Adiwardana,Minh-Thang Luong,David R. So,Jamie Hall,Noah Fiedel,Romal Thoppilan,Zi Yang,Apoorv Kulshreshtha,Gaurav Nemade,Yifeng Lu,Quoc V. Le +10 more
TL;DR: Meena, a multi-turn open-domain chatbot trained end-to-end on data mined and filtered from public domain social media conversations, is presented and a human evaluation metric called Sensibleness and Specificity Average (SSA) is proposed, which captures key elements of a human-like multi- turn conversation.
Journal ArticleDOI
Deep Learning--based Text Classification: A Comprehensive Review
TL;DR: This paper provided a comprehensive review of more than 150 deep learning-based models for text classification developed in recent years, and discussed their technical contributions, similarities, and strengths, and provided a quantitative analysis of the performance of different deep learning models on popular benchmarks.
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Multi-Task Deep Neural Networks for Natural Language Understanding
TL;DR: A Multi-Task Deep Neural Network (MT-DNN) for learning representations across multiple natural language understanding (NLU) tasks that allows domain adaptation with substantially fewer in-domain labels than the pre-trained BERT representations.
Proceedings ArticleDOI
DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation
Yizhe Zhang,Siqi Sun,Michel Galley,Yen-Chun Chen,Chris Brockett,Xiang Gao,Jianfeng Gao,Jingjing Liu,Bill Dolan +8 more
TL;DR: It is shown that conversational systems that leverage DialoGPT generate more relevant, contentful and context-consistent responses than strong baseline systems.
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