# Multilingual Extractive Reading Comprehension by Runtime Machine Translation

arXiv: https://arxiv.org/abs/1809.03275 · HTML: https://mlsanity.com/paper/1809.03275/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2018.

## Built on by (most cited first, 17 of 17)

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- [Improving Low-resource Reading Comprehension via Cross-lingual Transposition Rethinking](https://mlsanity.com/paper/2107.05002/) (2021)
- [A Multilingual Modeling Method for Span-Extraction Reading Comprehension](https://mlsanity.com/paper/2105.14880/) (2021)
- [Natural Answer Generation: From Factoid Answer to Full-length Answer using Grammar Correction](https://mlsanity.com/paper/2112.03849/) (2021)
- [Cross-Lingual Question Answering over Knowledge Base as Reading Comprehension](https://mlsanity.com/paper/2302.13241/) (2023)
- [SynDARin: Synthesising Datasets for Automated Reasoning in Low-Resource Languages](https://mlsanity.com/paper/2406.14425/) (2024)
- [Factuality Detection using Machine Translation -- a Use Case for German Clinical Text](https://mlsanity.com/paper/2308.08827/) (2023)

## Abstract (arXiv)

Despite recent work in Reading Comprehension (RC), progress has been mostly limited to English due to the lack of large-scale datasets in other languages. In this work, we introduce the first RC system for languages without RC training data. Given a target language without RC training data and a pivot language with RC training data (e.g. English), our method leverages existing RC resources in the pivot language by combining a competitive RC model in the pivot language with an attentive Neural Machine Translation (NMT) model. We first translate the data from the target to the pivot language, and then obtain an answer using the RC model in the pivot language. Finally, we recover the corresponding answer in the original language using soft-alignment attention scores from the NMT model. We create evaluation sets of RC data in two non-English languages, namely Japanese and French, to evaluate our method. Experimental results on these datasets show that our method significantly outperforms a back-translation baseline of a state-of-the-art product-level machine translation system.