Papers by Shenghui Li
Enhancing Conversational Search: Large Language Model-Aided Informative Query Rewriting (2023.findings-emnlp)
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| Challenge: | Existing approaches to rewrite context-dependent queries lack sufficient information for optimal retrieval performance. |
| Approach: | They propose to use large language models (LLMs) as query rewriters to generate informative queries through well-designed instructions. |
| Outcome: | The proposed approach improves performance on the QReCC dataset compared to human rewrites . |
MetaASSIST: Robust Dialogue State Tracking with Meta Learning (2022.emnlp-main)
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| Challenge: | Existing dialogue datasets contain lots of noise in their state annotations. |
| Approach: | They propose a framework to train robust dialogue state tracking models by combining pseudo and vanilla labels by a common weighting parameter. |
| Outcome: | The proposed framework achieves state-of-the-art accuracy of 80.10% on multiWOZ 2.4. |