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    <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.</abstract>
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%0 Conference Proceedings
%T Cross-lingual Text-to-SQL Semantic Parsing with Representation Mixup
%A Shi, Peng
%A Song, Linfeng
%A Jin, Lifeng
%A Mi, Haitao
%A Bai, He
%A Lin, Jimmy
%A Yu, Dong
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F shi-etal-2022-cross
%X 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.
%R 10.18653/v1/2022.findings-emnlp.388
%U https://aclanthology.org/2022.findings-emnlp.388
%U https://doi.org/10.18653/v1/2022.findings-emnlp.388
%P 5296-5306

Markdown (Informal)

[Cross-lingual Text-to-SQL Semantic Parsing with Representation Mixup](https://aclanthology.org/2022.findings-emnlp.388) (Shi et al., Findings 2022)

• Cross-lingual Text-to-SQL Semantic Parsing with Representation Mixup (Shi et al., Findings 2022)

ACL

• Peng Shi, Linfeng Song, Lifeng Jin, Haitao Mi, He Bai, Jimmy Lin, and Dong Yu. 2022. Cross-lingual Text-to-SQL Semantic Parsing with Representation Mixup. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 5296–5306, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.