Chunyang Xiao


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Grammatical Sequence Prediction for Real-Time Neural Semantic Parsing
Chunyang Xiao | Christoph Teichmann | Konstantine Arkoudas
Proceedings of the Workshop on Deep Learning and Formal Languages: Building Bridges

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


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Sequence-based Structured Prediction for Semantic Parsing
Chunyang Xiao | Marc Dymetman | Claire Gardent
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Orthogonality regularizer for question answering
Chunyang Xiao | Guillaume Bouchard | Marc Dymetman | Claire Gardent
Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics


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Reversibility reconsidered: finite-state factors for efficient probabilistic sampling in parsing and generation
Marc Dymetman | Sriram Venkatapathy | Chunyang Xiao
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing