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Cross-lingual Machine Reading Comprehension with Language Branch Knowledge Distillation
Cross-lingual Machine Reading Comprehension (CLMRC) remains a challengin...
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CalibreNet: Calibration Networks for Multilingual Sequence Labeling
Lack of training data in low-resource languages presents huge challenges...
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Multilingual Synthetic Question and Answer Generation for Cross-Lingual Reading Comprehension
We propose a simple method to generate large amounts of multilingual que...
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XCMRC: Evaluating Cross-lingual Machine Reading Comprehension
We present XCMRC, the first public cross-lingual language understanding ...
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Reading Comprehension in Czech via Machine Translation and Cross-lingual Transfer
Reading comprehension is a well studied task, with huge training dataset...
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EXAMS: A Multi-Subject High School Examinations Dataset for Cross-Lingual and Multilingual Question Answering
We propose EXAMS – a new benchmark dataset for cross-lingual and multili...
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Bilingual Text Extraction as Reading Comprehension
In this paper, we propose a method to extract bilingual texts automatica...
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Enhancing Answer Boundary Detection for Multilingual Machine Reading Comprehension
Multilingual pre-trained models could leverage the training data from a rich source language (such as English) to improve performance on low resource languages. However, the transfer quality for multilingual Machine Reading Comprehension (MRC) is significantly worse than sentence classification tasks mainly due to the requirement of MRC to detect the word level answer boundary. In this paper, we propose two auxiliary tasks in the fine-tuning stage to create additional phrase boundary supervision: (1) A mixed MRC task, which translates the question or passage to other languages and builds cross-lingual question-passage pairs; (2) A language-agnostic knowledge masking task by leveraging knowledge phrases mined from web. Besides, extensive experiments on two cross-lingual MRC datasets show the effectiveness of our proposed approach.
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