Designing the Business Conversation Corpus

Rikters, Matīss; Ri, Ryokan; Li, Tong; Nakazawa, Toshiaki

doi:10.18653/v1/d19-5204

Cited by 14 publications

(11 citation statements)

References 12 publications

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“…We used all the test sets in our previous work (Morishita et al, 2020), which included the Asian Scientific Paper Excerpt Corpus (AS-PEC) (Nakazawa et al, 2016), the Japanese-English Subtitle Corpus (JESC) (Pryzant et al, 2017), the Kyoto Free Translation Task (KFTT) (Neubig, 2011), and TED talks (tst2015) (Cettolo et al, 2012). We also evaluated our models on the Business Scene Dialogue Corpus (Rikters et al, 2019) to check whether they worked on conversations. We also added test sets from shared tasks: WMT 2020, 2021 news translation shared tasks (Barrault et al, 2020;Akhbardeh et al, 2021), WMT 2019, 2020 robustness shared tasks (Li et al, 2019;Specia et al, 2020), and the IWSLT 2021 simultaneous translation task (Anastasopoulos et al, 2021).…”

Section: Test Setsmentioning

confidence: 99%

JParaCrawl v3.0: A Large-scale English-Japanese Parallel Corpus

Murata¹,

Chousa²,

Suzuki³

et al. 2022

Preprint

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Most current machine translation models are mainly trained with parallel corpora, and their translation accuracy largely depends on the quality and quantity of the corpora. Although there are billions of parallel sentences for a few language pairs, effectively dealing with most language pairs is difficult due to a lack of publicly available parallel corpora. This paper creates a large parallel corpus for English-Japanese, a language pair for which only limited resources are available, compared to such resource-rich languages as English-German. It introduces a new web-based English-Japanese parallel corpus named JParaCrawl v3.0. Our new corpus contains more than 21 million unique parallel sentence pairs, which is more than twice as many as the previous JParaCrawl v2.0 corpus. Through experiments, we empirically show how our new corpus boosts the accuracy of machine translation models on various domains. The JParaCrawl v3.0 corpus will eventually be publicly available online for research purposes.

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Section: Test Setsmentioning

confidence: 99%

JParaCrawl v3.0: A Large-scale English-Japanese Parallel Corpus

Murata¹,

Chousa²,

Suzuki³

et al. 2022

Preprint

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“…This task is running successfully in WAT since 2019 and attracted many teams working on multimodal machine translation and image captioning in Indian languages (Nakazawa et al, 2019.…”

Section: English→hindi Multi-modal Taskmentioning

confidence: 99%

“…For the analysis, we use the Business Scene Dialogue Corpus (Rikters et al, 2019), which is a Japanese and English parallel corpus in the conversational domain. Besides the published data, we also use the in-house version of the corpus, which amounts to a total of 104,961 sentence pairs.…”

Section: Is Local Context Useful For Predictingmentioning

confidence: 99%

“…For evaluation, as we have mentioned before, we use BLEU as the primary evaluation metric. WAT also uses metrics such as RIBES , AM-FM and human evaluation (Nakazawa et al, 2019. All these metrics focus on different aspects of translations and may lead to different rankings for submissions, however this multi-metric evaluation helps us understand that there may not be one perfect model.…”

Section: Training and Evaluationmentioning

confidence: 99%

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Proceedings of the 8th Workshop on Asian Translation (WAT2021)

Nakazawa¹,

Goto²

2021

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Many Asian countries are rapidly growing these days and the importance of communicating and exchanging the information with these countries has intensified. To satisfy the demand for communication among these countries, machine translation technology is essential.Machine translation technology has rapidly evolved recently and it is seeing practical use especially between European languages. However, the translation quality of Asian languages is not that high compared to that of European languages, and machine translation technology for these languages has not reached a stage of proliferation yet. This is not only due to the lack of the language resources for Asian languages but also due to the lack of techniques to correctly transfer the meaning of sentences from/to Asian languages. Consequently, a place for gathering and sharing the resources and knowledge about Asian language translation is necessary to enhance machine translation research for Asian languages.

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“…Thus, a parallel document-and sentence-aligned conversation corpus would be advantageous in taking MT research in this field to the next stage. In this paper, we introduce a newly constructed Japanese-English conversation corpus that contains three sub-corpora: business scene dialogue (BSD) (Rikters et al 2019), Japanese translation of AMI meeting corpus (AMI 1 ) (Mc-Cowan et al 2005), and the Japanese translation of OntoNotes 5.0 (ON 2 ) (Weischedel et al 2011). The corpus contains multiperson conversations in various situations: business scenes, meetings under specific themes, broadcast conversations, and telephone conversations.…”

Section: Introductionmentioning

confidence: 99%

Japanese–English Conversation Parallel Corpus for Promoting Context-aware Machine Translation Research

Rikters

et al. 2021

Journal of Natural Language Processing

Self Cite

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Most machine translation (MT) research has focused on sentences as translation units (sentence-level MT), and has achieved acceptable translation quality for sentences where cross-sentential context is not required in mainly high-resourced languages. Recently, many researchers have worked on MT models that can consider a crosssentential context. These models are often called context-aware MT or documentlevel MT models. Document-level MT is difficult to 1) train with a small amount of document-level data; and 2) evaluate, as the main methods and datasets focus on sentence-level evaluation. To address the first issue, we present a Japanese-English conversation corpus in which the cross-sentential context is available. As for the second issue, we manually identify the main areas where sentence-level MT fails to produce adequate translations in the lack of context. We then create an evaluation set in which these phenomena are annotated to alleviate the automatic evaluation of document-level systems. We train MT models using our corpus to demonstrate how the use of context leads to improvements.

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Designing the Business Conversation Corpus

Cited by 14 publications

References 12 publications

JParaCrawl v3.0: A Large-scale English-Japanese Parallel Corpus

JParaCrawl v3.0: A Large-scale English-Japanese Parallel Corpus

Proceedings of the 8th Workshop on Asian Translation (WAT2021)

Japanese–English Conversation Parallel Corpus for Promoting Context-aware Machine Translation Research

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