@inproceedings{ait-elfqih-monti-2023-evaluation,
title = "On the Evaluation of Terminology Translation Errors in {NMT} and {PB}-{SMT} in the Legal Domain: a Study on the Translation of {A}rabic Legal Documents into {E}nglish and {F}rench",
author = "Ait ElFqih, Khadija and
Monti, Johanna",
editor = "Haddad, Amal Haddad and
Terryn, Ayla Rigouts and
Mitkov, Ruslan and
Rapp, Reinhard and
Zweigenbaum, Pierre and
Sharoff, Serge",
booktitle = "Proceedings of the Workshop on Computational Terminology in NLP and Translation Studies (ConTeNTS) Incorporating the 16th Workshop on Building and Using Comparable Corpora (BUCC)",
month = sep,
year = "2023",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd., Shoumen, Bulgaria",
url = "https://aclanthology.org/2023.contents-1.4",
pages = "26--35",
abstract = "In the translation process, terminological resources are used to solve translation problems, so information on terminological equivalence is crucial to make the most appropriate choices in terms of translation equivalence. In the context of Machine translation, indeed, neural models have improved the state-of-the-art in Machine Translation considerably in recent years. However, they still underperform in domain-specific fields and in under-resourced languages. This is particularly evident in translating legal terminology for Arabic, where current Machine Translation outputs do not adhere to the contextual, linguistic, cultural, and terminological constraints posed by translating legal terms in Arabic. In this paper, we conduct a comparative qualitative evaluation and comprehensive error analysis on legal terminology translation in Phrase-Based Statistical Machine Translation and Neural Machine Translation in two translation language pairs: Arabic-English and Arabic-French. We propose an error typology taking the legal terminology translation from Arabic into account. We demonstrate our findings, highlighting the strengths and weaknesses of both approaches in the area of legal terminology translation for Arabic. We also introduce a multilingual gold standard dataset that we developed using our Arabic legal corpus. This dataset serves as a reliable benchmark and/or reference during the evaluation process to decide the degree of adequacy and fluency of the Phrase-Based Statistical Machine Translation and Neural Machine Translation systems.",
}
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<abstract>In the translation process, terminological resources are used to solve translation problems, so information on terminological equivalence is crucial to make the most appropriate choices in terms of translation equivalence. In the context of Machine translation, indeed, neural models have improved the state-of-the-art in Machine Translation considerably in recent years. However, they still underperform in domain-specific fields and in under-resourced languages. This is particularly evident in translating legal terminology for Arabic, where current Machine Translation outputs do not adhere to the contextual, linguistic, cultural, and terminological constraints posed by translating legal terms in Arabic. In this paper, we conduct a comparative qualitative evaluation and comprehensive error analysis on legal terminology translation in Phrase-Based Statistical Machine Translation and Neural Machine Translation in two translation language pairs: Arabic-English and Arabic-French. We propose an error typology taking the legal terminology translation from Arabic into account. We demonstrate our findings, highlighting the strengths and weaknesses of both approaches in the area of legal terminology translation for Arabic. We also introduce a multilingual gold standard dataset that we developed using our Arabic legal corpus. This dataset serves as a reliable benchmark and/or reference during the evaluation process to decide the degree of adequacy and fluency of the Phrase-Based Statistical Machine Translation and Neural Machine Translation systems.</abstract>
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%0 Conference Proceedings
%T On the Evaluation of Terminology Translation Errors in NMT and PB-SMT in the Legal Domain: a Study on the Translation of Arabic Legal Documents into English and French
%A Ait ElFqih, Khadija
%A Monti, Johanna
%Y Haddad, Amal Haddad
%Y Terryn, Ayla Rigouts
%Y Mitkov, Ruslan
%Y Rapp, Reinhard
%Y Zweigenbaum, Pierre
%Y Sharoff, Serge
%S Proceedings of the Workshop on Computational Terminology in NLP and Translation Studies (ConTeNTS) Incorporating the 16th Workshop on Building and Using Comparable Corpora (BUCC)
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F ait-elfqih-monti-2023-evaluation
%X In the translation process, terminological resources are used to solve translation problems, so information on terminological equivalence is crucial to make the most appropriate choices in terms of translation equivalence. In the context of Machine translation, indeed, neural models have improved the state-of-the-art in Machine Translation considerably in recent years. However, they still underperform in domain-specific fields and in under-resourced languages. This is particularly evident in translating legal terminology for Arabic, where current Machine Translation outputs do not adhere to the contextual, linguistic, cultural, and terminological constraints posed by translating legal terms in Arabic. In this paper, we conduct a comparative qualitative evaluation and comprehensive error analysis on legal terminology translation in Phrase-Based Statistical Machine Translation and Neural Machine Translation in two translation language pairs: Arabic-English and Arabic-French. We propose an error typology taking the legal terminology translation from Arabic into account. We demonstrate our findings, highlighting the strengths and weaknesses of both approaches in the area of legal terminology translation for Arabic. We also introduce a multilingual gold standard dataset that we developed using our Arabic legal corpus. This dataset serves as a reliable benchmark and/or reference during the evaluation process to decide the degree of adequacy and fluency of the Phrase-Based Statistical Machine Translation and Neural Machine Translation systems.
%U https://aclanthology.org/2023.contents-1.4
%P 26-35
Markdown (Informal)
[On the Evaluation of Terminology Translation Errors in NMT and PB-SMT in the Legal Domain: a Study on the Translation of Arabic Legal Documents into English and French](https://aclanthology.org/2023.contents-1.4) (Ait ElFqih & Monti, ConTeNTS-WS 2023)
ACL