Enhancing Machine Translation by Integrating Linguistic Knowledge in the Word Alignment Module

Berrichi, Safae; Mazroui, Azzeddine

doi:10.1109/iscv49265.2020.9204328

Cited by 2 publications

(1 citation statement)

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“…The automatic bilingual word alignment being a non-trivial task in itself (Song et al, 2020;Berrichi and Mazroui, 2020), our detection of non-literal translations at phrase level is currently based on already aligned pairs. In future work, it is important to leverage the advances of automatic alignment to reduce the reliance on manual work.…”

Section: Discussionmentioning

confidence: 99%

Detecting Non-literal Translations by Fine-tuning Cross-lingual Pre-trained Language Models

Zhai

Illouz

Vilnat

2020

Proceedings of the 28th International Conference on Computational Linguistics

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Human-generated non-literal translations reflect the richness of human languages and are sometimes indispensable to ensure adequacy and fluency. Non-literal translations are difficult to produce even for human translators, especially for foreign language learners, and machine translations are still on the way to simulate human ones on this aspect. In order to foster the study on appropriate and creative non-literal translations, automatically detecting them in parallel corpora is an important step, which can benefit downstream NLP tasks or help to construct materials to teach translation. This article demonstrates that generic sentence representations produced by a pre-trained cross-lingual language model could be fine-tuned to solve this task. We show that there exists a moderate positive correlation between the prediction probability of being human translation and the non-literal translations' proportion in a sentence. The fine-tuning experiments show an accuracy of 80.16% when predicting the presence of non-literal translations in a sentence and an accuracy of 85.20% when distinguishing literal and non-literal translations at phrase level. We further conduct a linguistic error analysis and propose directions for future work.

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Section: Discussionmentioning

confidence: 99%