CGF: Constrained Generation Framework for Query Rewriting in Conversational AI
Jie Hao, Yang Liu, Xing Fan, Saurabh Gupta, Saleh Soltan, Rakesh Chada, Pradeep Natarajan, Chenlei Guo
Abstract
In conversational AI agents, Query Rewriting (QR) plays a crucial role in reducing user frictions and satisfying their daily demands. User frictions are caused by various reasons, such as errors in the conversational AI system, users’ accent or their abridged language. In this work, we present a novel Constrained Generation Framework (CGF) for query rewriting at both global and personalized levels. It is based on the encoder-decoder framework, where the encoder takes the query and its previous dialogue turns as the input to form a context-enhanced representation, and the decoder uses constrained decoding to generate the rewrites based on the pre-defined global or personalized constrained decoding space. Extensive offline and online A/B experiments show that the proposed CGF significantly boosts the query rewriting performance.
BibTeX
@inproceedings{hao-etal-2022-cgf,
title = "{CGF}: Constrained Generation Framework for Query Rewriting in Conversational {AI}",
author = "Hao, Jie and
Liu, Yang and
Fan, Xing and
Gupta, Saurabh and
Soltan, Saleh and
Chada, Rakesh and
Natarajan, Pradeep and
Guo, Chenlei and
Tur, Gokhan",
editor = "Li, Yunyao and
Lazaridou, Angeliki",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = dec,
year = "2022",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-industry.48/",
doi = "10.18653/v1/2022.emnlp-industry.48",
pages = "475--483"
}