@inproceedings{haque-etal-2019-investigating,
title = "Investigating Terminology Translation in Statistical and Neural Machine Translation: A Case Study on {E}nglish-to-{H}indi and {H}indi-to-{E}nglish",
author = "Haque, Rejwanul and
Hasanuzzaman, Md and
Way, Andy",
editor = "Mitkov, Ruslan and
Angelova, Galia",
booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
month = sep,
year = "2019",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd.",
url = "https://aclanthology.org/R19-1052",
doi = "10.26615/978-954-452-056-4_052",
pages = "437--446",
abstract = "Terminology translation plays a critical role in domain-specific machine translation (MT). In this paper, we conduct a comparative qualitative evaluation on terminology translation in phrase-based statistical MT (PB-SMT) and neural MT (NMT) in two translation directions: English-to-Hindi and Hindi-to-English. For this, we select a test set from a legal domain corpus and create a gold standard for evaluating terminology translation in MT. We also propose an error typology taking the terminology translation errors into consideration. We evaluate the MT systems{'} performance on terminology translation, and demonstrate our findings, unraveling strengths, weaknesses, and similarities of PB-SMT and NMT in the area of term translation.",
}
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%0 Conference Proceedings
%T Investigating Terminology Translation in Statistical and Neural Machine Translation: A Case Study on English-to-Hindi and Hindi-to-English
%A Haque, Rejwanul
%A Hasanuzzaman, Md
%A Way, Andy
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
%D 2019
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F haque-etal-2019-investigating
%X Terminology translation plays a critical role in domain-specific machine translation (MT). In this paper, we conduct a comparative qualitative evaluation on terminology translation in phrase-based statistical MT (PB-SMT) and neural MT (NMT) in two translation directions: English-to-Hindi and Hindi-to-English. For this, we select a test set from a legal domain corpus and create a gold standard for evaluating terminology translation in MT. We also propose an error typology taking the terminology translation errors into consideration. We evaluate the MT systems’ performance on terminology translation, and demonstrate our findings, unraveling strengths, weaknesses, and similarities of PB-SMT and NMT in the area of term translation.
%R 10.26615/978-954-452-056-4_052
%U https://aclanthology.org/R19-1052
%U https://doi.org/10.26615/978-954-452-056-4_052
%P 437-446
Markdown (Informal)
[Investigating Terminology Translation in Statistical and Neural Machine Translation: A Case Study on English-to-Hindi and Hindi-to-English](https://aclanthology.org/R19-1052) (Haque et al., RANLP 2019)
ACL