@inproceedings{yu2020few-shot,
author = {Yu, Shi and Liu, Jiahua and Yang, Jingqin and Xiong, Chenyan and Bennett, Paul and Gao, Jianfeng and Liu, Zhiyuan},
title = {Few-Shot Generative Conversational Query Rewriting},
booktitle = {SIGIR 2020},
year = {2020},
month = {June},
abstract = {Conversational query rewriting aims to reformulate a concise conversational query to a fully specified, context-independent query that can be effectively handled by existing information retrieval systems. This paper presents a few-shot generative approach to conversational query rewriting. We develop two methods, based on rules and self-supervised learning, to generate weak supervision data using large amounts of ad hoc search sessions, and to fine-tune GPT-2 to rewrite conversational queries. On the TREC Conversational Assistance Track, our weakly supervised GPT-2 rewriter improves the state-of-the-art ranking accuracy by 12%, only using very limited amounts of manual query rewrites. In the zero-shot learning setting, the rewriter still gives a comparable result to previous state-of-the-art systems. Our analyses reveal that GPT-2 effectively picks up the task syntax and learns to capture context dependencies, even for hard cases that involve group references and long-turn dependencies.},
url = {http://approjects.co.za/?big=en-us/research/publication/few-shot-generative-conversational-query-rewriting/},
note = {Best Short Paper Award},
}