Deep Chit-Chat: deep learning for chatbots (EMNLP 2018 Tutorial)
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by Dr Wei Wu (Microsoft Xiaolce - chatbot with 200 millions users in China) and Dr Rui Yan (Peking Univ) - Chit-chat (casual, non goal oriented) open-domain. Must be relevant to the context and diverse (informative) to be engaging. - why creating a chat? to prove an AI can speak like a human, commercial reasons, link to services. Task oriented vs non task oriented: this tutorial is about the second one. Retrieval based vs generation based. Basic knowledge of DL for chatbots: - word embeddings - sentence embeddings (CNN, RNN) - dialogue modeling: seq-to-seq with attention Response selection for retrieval based chatbots: - single turn response selection (slides 37-57) - framework 1: matching with seq embeddings - framework 2: matching with message-response interaction (46) - extension of 1: KnowledgeMatching with External Knowledge (53) - extension of 2: RepresentationsMatching with Multiple Levels of Representations (54) - insights from comparison between 1 and 2 (57) - multi turn response selection (62) - context is now: mess + history - again, 2 frameworks Emerging directions (79): - matching with better representations - Self-Attention (82) - fusing multiple types of repr. But how to fuse matters (83) - pre-training Learning a matching model for response selection (84) Generation based models for chatbots: - single turn generarion (89) - Basic generation model - seq2seq - Attention - Bi-directional modeling - multi turn generation - Contexts are important - Context sensitive models - Hierarchical context modeling - Latent variable modeling - Hierarchical memory networks Diversity in conversations (99) Content introducing (106) Additional elements (113) - Topics in cnversation - Emotions Persona in chat: - Persona - ... - Knowledge - Common sense RL and Adversarial learning in conversations (125) Evaluation (132) Future trends: - Reasoning in dialogues - X-grounded dialogues
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