Response-Anticipated Memory for On-Demand Knowledge Integration in Response Generation (2020.acl-main)
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| Challenge: | Neural conversation models generate appropriate but non-informative responses in general. |
| Approach: | They propose to construct a document memory with anticipated responses in mind using a teacher-student framework and a student's input. |
| Outcome: | The proposed model outperforms the state-of-the-art for the Conversing by Reading task. |
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| Challenge: | End-to-end neural models for conversational AI often assume that a response can be generated by considering only the knowledge acquired during training. |
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Conversing by Reading: Contentful Neural Conversation with On-demand Machine Reading (P19-1)
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Lianhui Qin, Michel Galley, Chris Brockett, Xiaodong Liu, Xiang Gao, Bill Dolan, Yejin Choi, Jianfeng Gao
| Challenge: | a new approach to contentful neural conversation is proposed . end-to-end models are effective in learning fluent responses, but their responses are often vacuous and uninformative. |
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Task-Oriented Conversation Generation Using Heterogeneous Memory Networks (D19-1)
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Learning to Abstract for Memory-augmented Conversational Response Generation (P19-1)
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| Challenge: | Existing generative models for open-domain chit-chat conversations lack informativeness and diversity. |
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Enhancing Neural Data-To-Text Generation Models with External Background Knowledge (D19-1)
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| Challenge: | Recent neural models for data-to-text generation rely on parallel pairs of data and text to learn writing knowledge. |
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| Challenge: | Existing work on how to generate relevant and informative responses is focusing on how dialogue systems generate information from large dialogue corpus. |
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Towards Exploiting Background Knowledge for Building Conversation Systems (D18-1)
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| Challenge: | Existing dialog datasets contain a sequence of utterances without any explicit background knowledge associated with them. |
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Attention-based Conditioning Methods for External Knowledge Integration (P19-1)
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| Challenge: | Existing approaches for incorporating external knowledge into deep neural networks (RNNs) lexicon features are used to concatenate external information into the input or hidden network layers. |
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Knowledge-Grounded Dialogue Generation with Pre-trained Language Models (2020.emnlp-main)
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| Challenge: | Empirical results indicate that pre-trained language models can significantly outperform state-of-the-art methods in both automatic evaluation and human judgment. |
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Generating Informative Conversational Response using Recurrent Knowledge-Interaction and Knowledge-Copy (2020.acl-main)
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| Challenge: | Knowledge-driven conversation approaches have attracted considerable research attention in recent years. |
| Approach: | They propose a method that integrates recurrent knowledge interaction among response decoding steps to incorporate appropriate knowledge. |
| Outcome: | The proposed method improves on two datasets Wizard-of-Wikipedia and DuConv with different knowledge formats and different languages. |