B ehance MT : A Machine Translation Corpus for Livestreaming Video Transcripts (2022)

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.

## introduction machine translation (mt) is an important and challenging task in natural language processing. early work solved the task via statistical models @xcite @xcite . recent work has made significant improvement via deep learning models @xcite @xcite @xcite ) that formalize mt as a text generation task, where an encoder is used to consume input text in a source language and a decoder is employed to generate the input's translation in a target language. in addition to the advance in model design, another factor contributing to the success of deep learning models is the creation of enormous mt corpora for model training such as wmt corpora @xcite , opus corpus @xcite and iwslt corpus @xcite . however, these corpora often contain formal-language texts such as books and news articles. this could lead to poor performance of the mt models, which are pretrained on such corpora, on informal-language text such as video transcripts. this is unfortunate as video transcripts are being generated at growing rate in international online video platforms such as youtube foot_0 , dailymotion foot_1 , and behance foot_2 . video transcript translation is thus important to improve access to the platforms' content for users who speak different languages. in this work, we aim to address this issue by introducing a novel mt corpus -behancemt for video transcript translation (vtt). behancemt contains transcripts collected from the behance platform and translations obtained by human annotators for 3 language pairs, where english is the source language and spanish, chinese, and arabic are the target languages. an mt system pretrained on formal-language corpora can then be finetuned on behancemt to improve its performance for vtt. to demonstrate this idea, we employ opusmt @xcite , which is a popular mt system pretrained on opus corpora. for each language pair, we finetune the pretrained opusmt on the behancemt training data and evaluate the model (called opusmt+) on the test data. experimental results show that opusmt+ consistently outperforms opusmt in all settings across the three language pairs for vtt. in addition, we compare opusmt+ with google-translate foot_3 . the significant improvement obtained by opusmt+ over googletranslate in @xmath0. to facilitate future work for vtt, we will publicly release the behancemt corpus. ## related work previous work has created different corpora for mt, such as wmt corpora @xcite , opus corpus @xcite and iwslt corpus @xcite . however, most of these corpora focus on formal-language settings. to the best of our knowledge, @xcite , which involves parallel ted talks, is the closest work to ours. however, ted talks are mostly presented in formal language. by contrast, behancemt is created based on transcripts of livestreaming videos, which are more informal. ## data in this section, we present how we collect, preprocess, and annotate video transcripts to create the behancemt corpus. ## model we employ opusmt @xcite as the main model to conduct experiments on the proposed behancemt corpus. opusmt uses the marian-nmt architecture @xcite and is pretrained on opus corpus @xcite to perform the translation task for different language pairs. for each of the three language pairs (i.e., @xmath1, @xmath2, @xmath3, we further finetune the pretrained bilingual opusmt model on the corresponding training data in behancemt. we denote the finetuned opusmt model as opusmt+. ## conclusion in this work, we present a novel corpus -be-hancemt for video transcript translation (vtt). behance contains parallel video transcripts for three language pairs, where english is the source language and spanish, arabic, and chinese are the target languages. our experiments with strong baselines on behancemt show that the proposed corpus is challenging and useful for vtt across the three language pairs.