Chat Translation Error Detection for Assisting Cross-lingual Communications
This work addresses the limitations of current machine chat translation methods to assist users in cross-lingual communications, but it is incremental as it builds on existing error detection approaches.
The paper tackles the problem of erroneous translations in cross-lingual chat communications by developing a system that detects such errors, using a trained error detector as a baseline and constructing a new Japanese-English bilingual chat corpus called BPersona-chat with crowdsourced quality ratings.
In this paper, we describe the development of a communication support system that detects erroneous translations to facilitate crosslingual communications due to the limitations of current machine chat translation methods. We trained an error detector as the baseline of the system and constructed a new Japanese-English bilingual chat corpus, BPersona-chat, which comprises multiturn colloquial chats augmented with crowdsourced quality ratings. The error detector can serve as an encouraging foundation for more advanced erroneous translation detection systems.