Designing the Business Conversation Corpus
This work addresses the problem of low-quality translation in conversational texts for business applications, but it is incremental as it focuses on a specific domain and dataset.
The paper tackles the challenge of machine translation for spoken dialogues by introducing a new Japanese-English business conversation parallel corpus, and demonstrates that incorporating this corpus into training improves translation quality.
While the progress of machine translation of written text has come far in the past several years thanks to the increasing availability of parallel corpora and corpora-based training technologies, automatic translation of spoken text and dialogues remains challenging even for modern systems. In this paper, we aim to boost the machine translation quality of conversational texts by introducing a newly constructed Japanese-English business conversation parallel corpus. A detailed analysis of the corpus is provided along with challenging examples for automatic translation. We also experiment with adding the corpus in a machine translation training scenario and show how the resulting system benefits from its use.