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Robust Neural Machine Translation for Clean and Noisy Speech Transcripts
2019
Zenodo
Neural machine translation models have shown to achieve high quality when trained and fed with well structured and punctuated input texts. Unfortunately, the latter condition is not met in spoken language translation, where the input is generated by an automatic speech recognition (ASR) system. In this paper, we study how to adapt a strong NMT system to make it robust to typical ASR errors. As in our application scenarios transcripts might be post-edited by human experts, we propose adaptation
doi:10.5281/zenodo.3524946
fatcat:55whr2u27rh5hocmm37ok4hbje