CL-ReLKT: Cross-lingual Language Knowledge Transfer for Multilingual Retrieval Question Answering

Anonymous

Paper Link: https://openreview.net/forum?id=y42xxJ_xx8

Paper Type: Long paper (up to eight pages of content + unlimited references and appendices)

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.

Copyright Consent Signature (type Name Or NA If Not Transferrable): peerat

Copyright Consent Name And Address: Peerat, Wangchan Valley 555 Moo 1 Payupnai 12120