Shenghui Li — accepted AI-conference papers

2023

Enhancing Conversational Search: Large Language Model-Aided Informative Query Rewriting

Fanghua Ye, Meng Fang, Shenghui Li, Emine Yilmaz

EMNLP 2023long findings

Query rewriting plays a vital role in enhancing conversational search by transforming context-dependent user queries into standalone forms. Existing approaches primarily leverage human-rewritten queries as labels to train query rewriting models. However, human rewrites may lack sufficient informatio…

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2022

MetaASSIST: Robust Dialogue State Tracking with Meta Learning

Fanghua Ye, Xi Wang, Jie Huang, Shenghui Li, Samuel Stern, Emine Yilmaz

EMNLP 2022main

Existing dialogue datasets contain lots of noise in their state annotations. Such noise can hurt model training and ultimately lead to poor generalization performance. A general framework named ASSIST has recently been proposed to train robust dialogue state tracking (DST) models. It introduces an a…

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