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    <abstract>Machine translation (MT) is an important task in natural language processing, which aims to translate a sentence in a source language to another sentence with the same/similar semantics in a target language. Despite the huge effort on building MT systems for different language pairs, most previous work focuses on formal-language settings, where text to be translated come from written sources such as books and news articles. As a result, such MT systems could fail to translate livestreaming video transcripts, where text is often shorter and might be grammatically incorrect. To overcome this issue, we introduce a novel MT corpus - BehanceMT for livestreaming video transcript translation. Our corpus contains parallel transcripts for 3 language pairs, where English is the source language and Spanish, Chinese, and Arabic are the target languages. Experimental results show that finetuning a pretrained MT model on BehanceMT significantly improves the performance of the model in translating video transcripts across 3 language pairs. In addition, the finetuned MT model outperforms GoogleTranslate in 2 out of 3 language pairs, further demonstrating the usefulness of our proposed dataset for video transcript translation. BehanceMT will be publicly released upon the acceptance of the paper.</abstract>
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%0 Conference Proceedings
%T BehanceMT: A Machine Translation Corpus for Livestreaming Video Transcripts
%A Nguyen, Minh Van
%A Dernoncourt, Franck
%A Nguyen, Thien
%Y Dernoncourt, Franck
%Y Nguyen, Thien Huu
%Y Lai, Viet Dac
%Y Veyseh, Amir Pouran Ben
%Y Bui, Trung H.
%Y Yoon, David Seunghyun
%S Proceedings of the First Workshop On Transcript Understanding
%D 2022
%8 October
%I International Conference on Computational Linguistics
%C Gyeongju, South Korea
%F nguyen-etal-2022-behancemt
%X Machine translation (MT) is an important task in natural language processing, which aims to translate a sentence in a source language to another sentence with the same/similar semantics in a target language. Despite the huge effort on building MT systems for different language pairs, most previous work focuses on formal-language settings, where text to be translated come from written sources such as books and news articles. As a result, such MT systems could fail to translate livestreaming video transcripts, where text is often shorter and might be grammatically incorrect. To overcome this issue, we introduce a novel MT corpus - BehanceMT for livestreaming video transcript translation. Our corpus contains parallel transcripts for 3 language pairs, where English is the source language and Spanish, Chinese, and Arabic are the target languages. Experimental results show that finetuning a pretrained MT model on BehanceMT significantly improves the performance of the model in translating video transcripts across 3 language pairs. In addition, the finetuned MT model outperforms GoogleTranslate in 2 out of 3 language pairs, further demonstrating the usefulness of our proposed dataset for video transcript translation. BehanceMT will be publicly released upon the acceptance of the paper.
%U https://aclanthology.org/2022.tu-1.4
%P 30-33

Markdown (Informal)

[BehanceMT: A Machine Translation Corpus for Livestreaming Video Transcripts](https://aclanthology.org/2022.tu-1.4) (Nguyen et al., TU 2022)

• BehanceMT: A Machine Translation Corpus for Livestreaming Video Transcripts (Nguyen et al., TU 2022)

ACL

• Minh Van Nguyen, Franck Dernoncourt, and Thien Nguyen. 2022. BehanceMT: A Machine Translation Corpus for Livestreaming Video Transcripts. In Proceedings of the First Workshop On Transcript Understanding, pages 30–33, Gyeongju, South Korea. International Conference on Computational Linguistics.