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        <title>Constrained Decoding for Technical Term Retention in English-Hindi MT</title>
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    <abstract>Technical terms may require special handling when the target audience is bilingual, depending on the cultural and educational norms of the society in question. In particular, certain translation scenarios may require “term retention” i.e. preserving of the source language technical terms in the target language output to produce a fluent and comprehensible code-switched sentence. We show that a standard transformer-based machine translation model can be adapted easily to perform this task with little or no damage to the general quality of its output. We present an English-to-Hindi model that is trained to obey a “retain” signal, i.e. it can perform the required code-mixing on a list of terms, possibly unseen, provided at runtime. We perform automatic evaluation using BLEU as well as F1 metrics on the list of retained terms; we also collect manual judgments on the quality of the output sentences.</abstract>
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%0 Conference Proceedings
%T Constrained Decoding for Technical Term Retention in English-Hindi MT
%A Bafna, Niyati
%A Vastl, Martin
%A Bojar, Ondřej
%Y Bandyopadhyay, Sivaji
%Y Devi, Sobha Lalitha
%Y Bhattacharyya, Pushpak
%S Proceedings of the 18th International Conference on Natural Language Processing (ICON)
%D 2021
%8 December
%I NLP Association of India (NLPAI)
%C National Institute of Technology Silchar, Silchar, India
%F bafna-etal-2021-constrained
%X Technical terms may require special handling when the target audience is bilingual, depending on the cultural and educational norms of the society in question. In particular, certain translation scenarios may require “term retention” i.e. preserving of the source language technical terms in the target language output to produce a fluent and comprehensible code-switched sentence. We show that a standard transformer-based machine translation model can be adapted easily to perform this task with little or no damage to the general quality of its output. We present an English-to-Hindi model that is trained to obey a “retain” signal, i.e. it can perform the required code-mixing on a list of terms, possibly unseen, provided at runtime. We perform automatic evaluation using BLEU as well as F1 metrics on the list of retained terms; we also collect manual judgments on the quality of the output sentences.
%U https://aclanthology.org/2021.icon-main.1
%P 1-6

Markdown (Informal)

[Constrained Decoding for Technical Term Retention in English-Hindi MT](https://aclanthology.org/2021.icon-main.1) (Bafna et al., ICON 2021)

• Constrained Decoding for Technical Term Retention in English-Hindi MT (Bafna et al., ICON 2021)

ACL

• Niyati Bafna, Martin Vastl, and Ondřej Bojar. 2021. Constrained Decoding for Technical Term Retention in English-Hindi MT. In Proceedings of the 18th International Conference on Natural Language Processing (ICON), pages 1–6, National Institute of Technology Silchar, Silchar, India. NLP Association of India (NLPAI).