@inproceedings{xiao-etal-2019-grammatical,
    title = "Grammatical Sequence Prediction for Real-Time Neural Semantic Parsing",
    author = "Xiao, Chunyang  and
      Teichmann, Christoph  and
      Arkoudas, Konstantine",
    editor = "Eisner, Jason  and
      Gall{\'e}, Matthias  and
      Heinz, Jeffrey  and
      Quattoni, Ariadna  and
      Rabusseau, Guillaume",
    booktitle = "Proceedings of the Workshop on Deep Learning and Formal Languages: Building Bridges",
    month = aug,
    year = "2019",
    address = "Florence",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W19-3902",
    doi = "10.18653/v1/W19-3902",
    pages = "14--23",
    abstract = "While sequence-to-sequence (seq2seq) models achieve state-of-the-art performance in many natural language processing tasks, they can be too slow for real-time applications. One performance bottleneck is predicting the most likely next token over a large vocabulary; methods to circumvent this bottleneck are a current research topic. We focus specifically on using seq2seq models for semantic parsing, where we observe that grammars often exist which specify valid formal representations of utterance semantics. By developing a generic approach for restricting the predictions of a seq2seq model to grammatically permissible continuations, we arrive at a widely applicable technique for speeding up semantic parsing. The technique leads to a 74{\%} speed-up on an in-house dataset with a large vocabulary, compared to the same neural model without grammatical restrictions",
}