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arXiv · 2107.05002

Improving Low-resource Reading Comprehension via Cross-lingual Transposition Rethinking

Gaochen Wu·Bin Xu·Yuxin Qin·Fei Kong·Bangchang Liu·Hongwen Zhao·Dejie Chang
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Abstract

Extractive Reading Comprehension (ERC) has made tremendous advances enabled by the availability of large-scale high-quality ERC training data. Despite of such rapid progress and widespread application, the datasets in languages other than high-resource languages such as English remain scarce. To address this issue, we propose a Cross-Lingual Transposition ReThinking (XLTT) model by modelling existing high-quality extractive reading comprehension datasets in a multilingual environment. To be specific, we present multilingual adaptive attention (MAA) to combine intra-attention and inter-attention to learn more general generalizable semantic and lexical knowledge from each pair of language families. Furthermore, to make full use of existing datasets, we adopt a new training framework to train our model by calculating task-level similarities between each existing dataset and target dataset. The experimental results show that our XLTT model surpasses six baselines on two multilingual ERC benchmarks, especially more effective for low-resource languages with 3.9 and 4.1 average improvement in F1 and EM, respectively.

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Gaochen Wu, Bin Xu, Yuxin Qin, Fei Kong, Bangchang Liu, Hongwen Zhao, Dejie Chang. 2021-07-11. Improving Low-resource Reading Comprehension via Cross-lingual Transposition Rethinking. https://arxiv.org/abs/2107.05002

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