@inproceedings{limkonchotiwat-etal-2022-cl,
title = "{CL}-{R}e{LKT}: Cross-lingual Language Knowledge Transfer for Multilingual Retrieval Question Answering",
author = "Limkonchotiwat, Peerat and
Ponwitayarat, Wuttikorn and
Udomcharoenchaikit, Can and
Chuangsuwanich, Ekapol and
Nutanong, Sarana",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-naacl.165",
doi = "10.18653/v1/2022.findings-naacl.165",
pages = "2141--2155",
abstract = "Cross-Lingual Retrieval Question Answering (CL-ReQA) is concerned with retrieving answer documents or passages to a question written in a different language. A common approach to CL-ReQA is to create a multilingual sentence embedding space such that question-answer pairs across different languages are close to each other. In this paper, we propose a novel CL-ReQA method utilizing the concept of language knowledge transfer and a new cross-lingual consistency training technique to create a multilingual embedding space for ReQA. To assess the effectiveness of our work, we conducted comprehensive experiments on CL-ReQA and a downstream task, machine reading QA. We compared our proposed method with the current state-of-the-art solutions across three public CL-ReQA corpora. Our method outperforms competitors in 19 out of 21 settings of CL-ReQA. When used with a downstream machine reading QA task, our method outperforms the best existing language-model-based method by 10{\%} in F1 while being 10 times faster in sentence embedding computation. The code and models are available at \url{https://github.com/mrpeerat/CL-ReLKT}.",
}
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<abstract>Cross-Lingual Retrieval Question Answering (CL-ReQA) is concerned with retrieving answer documents or passages to a question written in a different language. A common approach to CL-ReQA is to create a multilingual sentence embedding space such that question-answer pairs across different languages are close to each other. In this paper, we propose a novel CL-ReQA method utilizing the concept of language knowledge transfer and a new cross-lingual consistency training technique to create a multilingual embedding space for ReQA. To assess the effectiveness of our work, we conducted comprehensive experiments on CL-ReQA and a downstream task, machine reading QA. We compared our proposed method with the current state-of-the-art solutions across three public CL-ReQA corpora. Our method outperforms competitors in 19 out of 21 settings of CL-ReQA. When used with a downstream machine reading QA task, our method outperforms the best existing language-model-based method by 10% in F1 while being 10 times faster in sentence embedding computation. The code and models are available at https://github.com/mrpeerat/CL-ReLKT.</abstract>
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%0 Conference Proceedings
%T CL-ReLKT: Cross-lingual Language Knowledge Transfer for Multilingual Retrieval Question Answering
%A Limkonchotiwat, Peerat
%A Ponwitayarat, Wuttikorn
%A Udomcharoenchaikit, Can
%A Chuangsuwanich, Ekapol
%A Nutanong, Sarana
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Findings of the Association for Computational Linguistics: NAACL 2022
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F limkonchotiwat-etal-2022-cl
%X Cross-Lingual Retrieval Question Answering (CL-ReQA) is concerned with retrieving answer documents or passages to a question written in a different language. A common approach to CL-ReQA is to create a multilingual sentence embedding space such that question-answer pairs across different languages are close to each other. In this paper, we propose a novel CL-ReQA method utilizing the concept of language knowledge transfer and a new cross-lingual consistency training technique to create a multilingual embedding space for ReQA. To assess the effectiveness of our work, we conducted comprehensive experiments on CL-ReQA and a downstream task, machine reading QA. We compared our proposed method with the current state-of-the-art solutions across three public CL-ReQA corpora. Our method outperforms competitors in 19 out of 21 settings of CL-ReQA. When used with a downstream machine reading QA task, our method outperforms the best existing language-model-based method by 10% in F1 while being 10 times faster in sentence embedding computation. The code and models are available at https://github.com/mrpeerat/CL-ReLKT.
%R 10.18653/v1/2022.findings-naacl.165
%U https://aclanthology.org/2022.findings-naacl.165
%U https://doi.org/10.18653/v1/2022.findings-naacl.165
%P 2141-2155
Markdown (Informal)
[CL-ReLKT: Cross-lingual Language Knowledge Transfer for Multilingual Retrieval Question Answering](https://aclanthology.org/2022.findings-naacl.165) (Limkonchotiwat et al., Findings 2022)
ACL