CL-ReLKT: Cross-lingual Language Knowledge Transfer for Multilingual Retrieval Question Answering
Peerat Limkonchotiwat, Wuttikorn Ponwitayarat, Can Udomcharoenchaikit, Ekapol Chuangsuwanich, Sarana Y. Nutanong · Findings of the Association for Computational Linguistics: NAACL 2022 · 2022
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 questionanswer 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.