%0 Conference Proceedings %T Improving Black-box Speech Recognition using Semantic Parsing %A Corona, Rodolfo %A Thomason, Jesse %A Mooney, Raymond %Y Kondrak, Greg %Y Watanabe, Taro %S Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers) %D 2017 %8 November %I Asian Federation of Natural Language Processing %C Taipei, Taiwan %F corona-etal-2017-improving %X Speech is a natural channel for human-computer interaction in robotics and consumer applications. Natural language understanding pipelines that start with speech can have trouble recovering from speech recognition errors. Black-box automatic speech recognition (ASR) systems, built for general purpose use, are unable to take advantage of in-domain language models that could otherwise ameliorate these errors. In this work, we present a method for re-ranking black-box ASR hypotheses using an in-domain language model and semantic parser trained for a particular task. Our re-ranking method significantly improves both transcription accuracy and semantic understanding over a state-of-the-art ASR’s vanilla output. %U https://aclanthology.org/I17-2021 %P 122-127