# Enhancing Answer Boundary Detection for Multilingual Machine Reading Comprehension

arXiv: https://arxiv.org/abs/2004.14069 · HTML: https://mlsanity.com/paper/2004.14069/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2020.

## Built on by (most cited first, 11 of 11)

- [BenchMAX: A Comprehensive Multilingual Evaluation Suite for Large Language Models](https://mlsanity.com/paper/2502.07346/) (2025)
- [Reinforced Iterative Knowledge Distillation for Cross-Lingual Named Entity Recognition](https://mlsanity.com/paper/2106.00241/) (2021)
- [From Good to Best: Two-Stage Training for Cross-lingual Machine Reading Comprehension](https://mlsanity.com/paper/2112.04735/) (2021)
- [Bridging the Gap between Language Models and Cross-Lingual Sequence Labeling](https://mlsanity.com/paper/2204.05210/) (2022)
- [Learning Disentangled Semantic Representations for Zero-Shot Cross-Lingual Transfer in Multilingual Machine Reading Comprehension](https://mlsanity.com/paper/2204.00996/) (2022)
- [Zero-Shot Cross-Lingual Machine Reading Comprehension via Inter-sentence Dependency Graph](https://mlsanity.com/paper/2112.00503/) (2021)
- [Span Detection for Aspect-Based Sentiment Analysis in Vietnamese](https://mlsanity.com/paper/2110.07833/) (2021)
- [Cross-Lingual Question Answering over Knowledge Base as Reading Comprehension](https://mlsanity.com/paper/2302.13241/) (2023)
- [Promoting Generalized Cross-lingual Question Answering in Few-resource Scenarios via Self-knowledge Distillation](https://mlsanity.com/paper/2309.17134/) (2023)
- [ListReader: Extracting List-form Answers for Opinion Questions](https://mlsanity.com/paper/2110.11692/) (2021)
- [Sharing, Teaching and Aligning: Knowledgeable Transfer Learning for Cross-Lingual Machine Reading Comprehension](https://mlsanity.com/paper/2311.06758/) (2023)

## Abstract (arXiv)

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.