  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.
