Assessing the Tolerance of Neural Machine Translation Systems Against Speech Recognition Errors
This work addresses the problem of improving translation accuracy for spoken language data, which is incremental as it builds on existing NMT research by testing specific encoding methods.
The paper investigates the robustness of neural machine translation (NMT) systems when translating automatic speech recognition (ASR) outputs with errors, comparing NMT encoder-decoder models against a phrase-based baseline to identify which ASR phenomena are better handled by NMT.
Machine translation systems are conventionally trained on textual resources that do not model phenomena that occur in spoken language. While the evaluation of neural machine translation systems on textual inputs is actively researched in the literature , little has been discovered about the complexities of translating spoken language data with neural models. We introduce and motivate interesting problems one faces when considering the translation of automatic speech recognition (ASR) outputs on neural machine translation (NMT) systems. We test the robustness of sentence encoding approaches for NMT encoder-decoder modeling, focusing on word-based over byte-pair encoding. We compare the translation of utterances containing ASR errors in state-of-the-art NMT encoder-decoder systems against a strong phrase-based machine translation baseline in order to better understand which phenomena present in ASR outputs are better represented under the NMT framework than approaches that represent translation as a linear model.