Robustness of Multi-Source MT to Transcription Errors
Dominik Macháček Peter Polák Ondřej Bojar Raj Dabre
Automatic speech translation is sensitive to speech recognition errors, but
in a multilingual scenario, the same content may be available in various
languages via simultaneous interpreting, dubbing or subtitling. In this paper,
we hypothesize that leveraging multiple sources will improve translation
quality if the sources complement one another in terms of correct information
they contain. To this end, we first show that on a 10-hour ESIC corpus, the ASR
errors in the original English speech and its simultaneous interpreting into
German and Czech are mutually independent. We then use two sources, English and
German, in a multi-source setting for translation into Czech to establish its
robustness to ASR errors. Furthermore, we observe this robustness when
translating both noisy sources together in a simultaneous translation setting.
Our results show that multi-source neural machine translation has the potential
to be useful in a real-time simultaneous translation setting, thereby
motivating further investigation in this area.