| Challenge: | Existing methods for dialog generation are limited and short at generalization. |
| Approach: | They propose a generalizable dialog generation approach that adapts multi-turn reasoning to generate responses by taking current conversation session context as a document and current query as 'question' they separate the single memory used for document comprehension into different groups for speaker-specific topic and opinion embedding. |
| Outcome: | Experiments on Japanese 10-sentence (5-round) conversation modeling show that multi-turn reasoning can produce more diverse and acceptable responses than state-of-the-art single-turn and non-reasoning baselines. |
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| Challenge: | Using a set of algorithms, we can generate large dialogue corpus from Reddit. |
| Approach: | They propose to automatically convert posts and their comments from discussion forums such as Reddit into multi-turn dialogues. |
| Outcome: | The proposed methods improve on the baseline method by 36.3% . the best method shows an improvement of 36.6% over the previous one . |
Learning a Simple and Effective Model for Multi-turn Response Generation with Auxiliary Tasks (2020.emnlp-main)
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| Challenge: | Existing approaches to multi-turn response generation for open-domain dialogues have a complexity problem . auxiliary tasks that relate to context understanding can guide the learning of the generation model . |
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Dual Dynamic Memory Network for End-to-End Multi-turn Task-oriented Dialog Systems (2020.coling-main)
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| Challenge: | Existing task-oriented dialog systems struggle to dynamically model long dialog context for interactions and effectively incorporate knowledge base (KB) information into dialog generation. |
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DIALOGPT : Large-Scale Generative Pre-training for Conversational Response Generation (2020.acl-demos)
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Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan
| Challenge: | DIALOGPT is a large, tunable neural conversational response generation model . trained on 147M conversation-like exchanges extracted from Reddit comment chains . |
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| Challenge: | Existing research on multi-turn spoken conversations focuses on reading comprehension of passages . interactivity of spoken content can cause lower information density and topic diffusion . |
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Do Neural Dialog Systems Use the Conversation History Effectively? An Empirical Study (P19-1)
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| Challenge: | Neural generative models are becoming more popular when building conversational agents. |
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Adaptive Parameterization for Neural Dialogue Generation (D19-1)
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| Challenge: | Existing models of open-domain dialogue generate responses based on sequence-to-sequence paradigms. |
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Infusing Context and Knowledge Awareness in Multi-turn Dialog Understanding (2023.findings-eacl)
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Unsupervised Discrete Sentence Representation Learning for Interpretable Neural Dialog Generation (P18-1)
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| Challenge: | Existing encoder-decoder dialog models cannot output interpretable actions as in traditional systems. |
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FCM: A Fine-grained Comparison Model for Multi-turn Dialogue Reasoning (2021.findings-emnlp)
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| Challenge: | Existing neural dialogue models only capture syntactic and semantic information, but fail to model the logical consistency between the dialogue history and the generated response. |
| Approach: | They propose a fine-grained comparison model to capture syntactic and semantic information and then compare each candidate's representation with the whole history to obtain a history consistency representation. |
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