Investigating Terminology Translation in Statistical and Neural Machine Translation: A Case Study on English-to-Hindi and Hindi-to-English

Rejwanul Haque, Md Hasanuzzaman, Andy Way


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.
Anthology ID:
R19-1052
Volume:
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
Month:
September
Year:
2019
Address:
Varna, Bulgaria
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
437–446
Language:
URL:
https://aclanthology.org/R19-1052
DOI:
10.26615/978-954-452-056-4_052
Bibkey:
Cite (ACL):
Rejwanul Haque, Md Hasanuzzaman, and Andy Way. 2019. Investigating Terminology Translation in Statistical and Neural Machine Translation: A Case Study on English-to-Hindi and Hindi-to-English. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 437–446, Varna, Bulgaria. INCOMA Ltd..
Cite (Informal):
Investigating Terminology Translation in Statistical and Neural Machine Translation: A Case Study on English-to-Hindi and Hindi-to-English (Haque et al., RANLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/R19-1052.pdf