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Assessing the Tolerance of Neural Machine Translation Systems Against Speech Recognition Errors
[article]
2019
arXiv
pre-print
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
arXiv:1904.10997v1
fatcat:dqx755bvavdmlo2wz4rftgw42y