Cross-lingual Text-to-SQL Semantic Parsing with Representation Mixup
Peng Shi, Linfeng Song, Lifeng Jin, Haitao Mi, He Bai, Jimmy Lin, Dong Yu
Abstract
We focus on the cross-lingual Text-to-SQL semantic parsing task,where the parsers are expected to generate SQL for non-English utterances based on English database schemas.Intuitively, English translation as side information is an effective way to bridge the language gap,but noise introduced by the translation system may affect parser effectiveness.In this work, we propose a Representation Mixup Framework (Rex) for effectively exploiting translations in the cross-lingual Text-to-SQL task.Particularly, it uses a general encoding layer, a transition layer, and a target-centric layer to properly guide the information flow of the English translation.Experimental results on CSpider and VSpider show that our framework can benefit from cross-lingual training and improve the effectiveness of semantic parsers, achieving state-of-the-art performance.
BibTeX
@inproceedings{shi-etal-2022-cross,
title = "Cross-lingual Text-to-{SQL} Semantic Parsing with Representation Mixup",
author = "Shi, Peng and
Song, Linfeng and
Jin, Lifeng and
Mi, Haitao and
Bai, He and
Lin, Jimmy and
Yu, Dong",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.388/",
doi = "10.18653/v1/2022.findings-emnlp.388",
pages = "5296--5306"
}