Improving Cross-Lingual Reading Comprehension with Self-Training

• URL: http://arxiv.org/abs/2105.03627v1
• Date: Sat, 8 May 2021 08:04:30 GMT
• Title: Improving Cross-Lingual Reading Comprehension with Self-Training
• Authors: Wei-Cheng Huang, Chien-yu Huang, Hung-yi Lee
• Abstract summary: Current state-of-the-art models even surpass human performance on several benchmarks. Previous works have revealed the abilities of pre-trained multilingual models for zero-shot cross-lingual reading comprehension. This paper further utilized unlabeled data to improve the performance.
• Score: 62.73937175625953
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Substantial improvements have been made in machine reading comprehension, where the machine answers questions based on a given context. Current state-of-the-art models even surpass human performance on several benchmarks. However, their abilities in the cross-lingual scenario are still to be explored. Previous works have revealed the abilities of pre-trained multilingual models for zero-shot cross-lingual reading comprehension. In this paper, we further utilized unlabeled data to improve the performance. The model is first supervised-trained on source language corpus, and then self-trained with unlabeled target language data. The experiment results showed improvements for all languages, and we also analyzed how self-training benefits cross-lingual reading comprehension in qualitative aspects.

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