From Good to Best: Two-Stage Training for Cross-lingual Machine Reading Comprehension

• URL: http://arxiv.org/abs/2112.04735v1
• Date: Thu, 9 Dec 2021 07:31:15 GMT
• Title: From Good to Best: Two-Stage Training for Cross-lingual Machine Reading Comprehension
• Authors: Nuo Chen, Linjun Shou, Min Gong, Jian Pei, Daxin Jiang
• Abstract summary: We develop a two-stage approach to enhance the model performance. The first stage targets at recall: we design a hard-learning (HL) algorithm to maximize the likelihood that the top-k predictions contain the accurate answer. The second stage focuses on precision: an answer-aware contrastive learning mechanism is developed to learn the fine difference between the accurate answer and other candidates.
• Score: 51.953428342923885
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cross-lingual Machine Reading Comprehension (xMRC) is challenging due to the lack of training data in low-resource languages. The recent approaches use training data only in a resource-rich language like English to fine-tune large-scale cross-lingual pre-trained language models. Due to the big difference between languages, a model fine-tuned only by a source language may not perform well for target languages. Interestingly, we observe that while the top-1 results predicted by the previous approaches may often fail to hit the ground-truth answers, the correct answers are often contained in the top-k predicted results. Based on this observation, we develop a two-stage approach to enhance the model performance. The first stage targets at recall: we design a hard-learning (HL) algorithm to maximize the likelihood that the top-k predictions contain the accurate answer. The second stage focuses on precision: an answer-aware contrastive learning (AA-CL) mechanism is developed to learn the fine difference between the accurate answer and other candidates. Our extensive experiments show that our model significantly outperforms a series of strong baselines on two cross-lingual MRC benchmark datasets.

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