Grounding in social media: An approach to building a chit-chat dialogue model (2022.naacl-srw)
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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. |
| Approach: | They propose to use social media comments to improve the raw conversation ability of open-domain dialogue systems. |
| Outcome: | The proposed model improves the raw conversation ability of open-domain dialogue systems by mimicking human responses through casual interactions found on social media. |
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Jungwoo Lim, Myunghoon Kang, Yuna Hur, Seung Won Jeong, Jinsung Kim, Yoonna Jang, Dongyub Lee, Hyesung Ji, DongHoon Shin, Seungryong Kim, Heuiseok Lim
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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: | a personalized dialogue system can generate user-customized responses based on long-term memory about the user's persona. |
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| Challenge: | Existing approaches to train grounded dialogues require large amounts of data. |
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| Challenge: | Existing knowledge-grounded dialogue generation models face the hallucination problem . Existing models generate inappropriate knowledge and generate inconsistent responses . |
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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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| Challenge: | Existing generative dialogue models lack coherence and are content poor . however, current models lack the capacity to handle large unstructured knowledge sources. |
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SalesBot: Transitioning from Chit-Chat to Task-Oriented Dialogues (2022.acl-long)
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| Challenge: | Until now, researchers have separated open-domain and task-oriented dialogues into two different types due to their different purposes. |
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Summary Grounded Conversation Generation (2021.findings-acl)
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| Challenge: | Existing datasets for conversation summarization are small due to the lack of large-scale datasets. |
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