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. |
| Approach: | They propose to use movie chats to generate responses by copying unstructured background knowledge . they use a dataset of 9K conversations to test whether responses are generated by copy-and-modify models . |
| Outcome: | The proposed model mimics human process of conversing by copying and/or modifying sentences from unstructured background knowledge. |
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| Challenge: | Existing models for dialogue generation lack the flexibility to handle such freedoms. |
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| Challenge: | Existing open-domain dialogue models fail to capture and utilize external knowledge, leading to repetitive or generic responses to unseen utterances. |
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| Challenge: | Existing datasets for conversation summarization are small due to the lack of large-scale datasets. |
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Extending Neural Generative Conversational Model using External Knowledge Sources (D18-1)
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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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A Synthetic Data Generation Framework for Grounded Dialogues (2023.acl-long)
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| Challenge: | Existing approaches to train grounded dialogues require large amounts of data. |
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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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| Challenge: | Existing pre-training models for dialogue generation have been proven effective for a wide range of tasks. |
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| Challenge: | Recent advances in large-scale pre-training provide large models with the potential to learn knowledge from the raw text. |
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