Post-editing of Technical Terms based on Bilingual Example Sentences

Elsie K. Y. Chan; John S. Y. Lee; Chester Cheng; Benjamin K. Tsou

Post-editing of Technical Terms based on Bilingual Example Sentences

Elsie K. Y. Chan, John S. Y. Lee, Chester Cheng, Benjamin K. Tsou

Department of Linguistics and Translation

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

1 Citation (Scopus)

97 Downloads (CityUHK Scholars)

Abstract

As technical fields become ever more specialized, and with continuous emergence of novel technical terms, it may not be always possible to avail of bilingual experts in the field to perform translation. This paper investigates the performance of bilingual non-experts in Computer-Assisted Translation. The translators were asked to identify and correct errors in MT output of technical terms in patent materials, aided only by example bilingual sentences. Targeting English-to-Chinese translation, we automatically extract the example sentences from a bilingual corpus of English and Chinese patents. We identify the most frequent translation candidates of a term, and then select the most relevant example sentences for each candidate according to semantic similarity. Even when given only two example sentences for each translation candidate, the non-expert translators were able to post-edit effectively, correcting 67.2% of the MT errors while mistakenly revising correct MT output in only 17% of the cases. © 2023 The authors.

Original language	English
Title of host publication	Proceedings of Machine Translation Summit XIX
Subtitle of host publication	Vol. 1: Research Track
Publisher	Asia-Pacific Association for Machine Translation
Pages	385-392
Volume	1
ISBN (Print)	9780000000002
Publication status	Published - Sept 2023
Event	Machine Translation Summit XIX (MT Summit 2023) - Studio City, Macau, China Duration: 4 Sept 2023 → 8 Sept 2023 https://mtsummit2023.scimeeting.cn/en/web/index/15680_ https://aclanthology.org/2023.mtsummit-research

Publication series

Name	MT Summit - Proceedings of Machine Translation Summit

Conference

Conference	Machine Translation Summit XIX (MT Summit 2023)
Abbreviated title	MTS 2023
Country/Territory	China
City	Macau
Period	4/09/23 → 8/09/23
Internet address	https://mtsummit2023.scimeeting.cn/en/web/index/15680_ https://aclanthology.org/2023.mtsummit-research

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This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/

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@inproceedings{7dbdbf6f4b074713916b7679887df914,

title = "Post-editing of Technical Terms based on Bilingual Example Sentences",

abstract = "As technical fields become ever more specialized, and with continuous emergence of novel technical terms, it may not be always possible to avail of bilingual experts in the field to perform translation. This paper investigates the performance of bilingual non-experts in Computer-Assisted Translation. The translators were asked to identify and correct errors in MT output of technical terms in patent materials, aided only by example bilingual sentences. Targeting English-to-Chinese translation, we automatically extract the example sentences from a bilingual corpus of English and Chinese patents. We identify the most frequent translation candidates of a term, and then select the most relevant example sentences for each candidate according to semantic similarity. Even when given only two example sentences for each translation candidate, the non-expert translators were able to post-edit effectively, correcting 67.2% of the MT errors while mistakenly revising correct MT output in only 17% of the cases. {\textcopyright} 2023 The authors.",

author = "Chan, {Elsie K. Y.} and Lee, {John S. Y.} and Chester Cheng and Tsou, {Benjamin K.}",

year = "2023",

month = sep,

language = "English",

isbn = "9780000000002",

volume = "1",

series = "MT Summit - Proceedings of Machine Translation Summit",

publisher = "Asia-Pacific Association for Machine Translation",

pages = "385--392",

booktitle = "Proceedings of Machine Translation Summit XIX",

note = "Machine Translation Summit XIX (MT Summit 2023), MTS 2023 ; Conference date: 04-09-2023 Through 08-09-2023",

url = "https://mtsummit2023.scimeeting.cn/en/web/index/15680_, https://aclanthology.org/2023.mtsummit-research",

}

Chan, EKY, Lee, JSY, Cheng, C & Tsou, BK 2023, Post-editing of Technical Terms based on Bilingual Example Sentences. in Proceedings of Machine Translation Summit XIX: Vol. 1: Research Track. vol. 1, MT Summit - Proceedings of Machine Translation Summit, Asia-Pacific Association for Machine Translation, pp. 385-392, Machine Translation Summit XIX (MT Summit 2023), Macau, China, 4/09/23.

Post-editing of Technical Terms based on Bilingual Example Sentences. / Chan, Elsie K. Y.; Lee, John S. Y.; Cheng, Chester et al.
Proceedings of Machine Translation Summit XIX: Vol. 1: Research Track. Vol. 1 Asia-Pacific Association for Machine Translation, 2023. p. 385-392 (MT Summit - Proceedings of Machine Translation Summit).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

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T1 - Post-editing of Technical Terms based on Bilingual Example Sentences

AU - Chan, Elsie K. Y.

AU - Lee, John S. Y.

AU - Cheng, Chester

AU - Tsou, Benjamin K.

PY - 2023/9

Y1 - 2023/9

N2 - As technical fields become ever more specialized, and with continuous emergence of novel technical terms, it may not be always possible to avail of bilingual experts in the field to perform translation. This paper investigates the performance of bilingual non-experts in Computer-Assisted Translation. The translators were asked to identify and correct errors in MT output of technical terms in patent materials, aided only by example bilingual sentences. Targeting English-to-Chinese translation, we automatically extract the example sentences from a bilingual corpus of English and Chinese patents. We identify the most frequent translation candidates of a term, and then select the most relevant example sentences for each candidate according to semantic similarity. Even when given only two example sentences for each translation candidate, the non-expert translators were able to post-edit effectively, correcting 67.2% of the MT errors while mistakenly revising correct MT output in only 17% of the cases. © 2023 The authors.

AB - As technical fields become ever more specialized, and with continuous emergence of novel technical terms, it may not be always possible to avail of bilingual experts in the field to perform translation. This paper investigates the performance of bilingual non-experts in Computer-Assisted Translation. The translators were asked to identify and correct errors in MT output of technical terms in patent materials, aided only by example bilingual sentences. Targeting English-to-Chinese translation, we automatically extract the example sentences from a bilingual corpus of English and Chinese patents. We identify the most frequent translation candidates of a term, and then select the most relevant example sentences for each candidate according to semantic similarity. Even when given only two example sentences for each translation candidate, the non-expert translators were able to post-edit effectively, correcting 67.2% of the MT errors while mistakenly revising correct MT output in only 17% of the cases. © 2023 The authors.

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VL - 1

T3 - MT Summit - Proceedings of Machine Translation Summit

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BT - Proceedings of Machine Translation Summit XIX

PB - Asia-Pacific Association for Machine Translation

T2 - Machine Translation Summit XIX (MT Summit 2023)

Y2 - 4 September 2023 through 8 September 2023

ER -

Post-editing of Technical Terms based on Bilingual Example Sentences

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