Controlling Text Complexity in Neural Machine Translation

Sweta Agrawal; Marine Carpuat

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arXiv · 1911.00835

Controlling Text Complexity in Neural Machine Translation

Sweta Agrawal·Marine Carpuat

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Abstract

This work introduces a machine translation task where the output is aimed at audiences of different levels of target language proficiency. We collect a high quality dataset of news articles available in English and Spanish, written for diverse grade levels and propose a method to align segments across comparable bilingual articles. The resulting dataset makes it possible to train multi-task sequence-to-sequence models that translate Spanish into English targeted at an easier reading grade level than the original Spanish. We show that these multi-task models outperform pipeline approaches that translate and simplify text independently.

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Sweta Agrawal, Marine Carpuat. 2019-11-03. Controlling Text Complexity in Neural Machine Translation. https://arxiv.org/abs/1911.00835

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