Data Recombination for Neural Semantic Parsing

Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pages 12–22, Berlin, Germany, August 7-12, 2016. c⃝2016 Association for Computational Linguistics Data Recombination for Neural Semantic Parsing Robin Jia Computer Science Department Stanford University robinjia@stanford.edu Percy Liang Computer Science Department Stanford University pliang@cs.stanford.edu Abstract Modeling crisp logical regularities is cru- cial in semantic parsing, making it difficult for neural models with no task-specific prior knowledge to achieve good results. In this paper, we introduce data recom- bination, a novel framework for inject- ing such prior knowledge into a model. From the training data, we induce a high- precision synchronous context-free gram- mar, which captures important conditional independence properties commonly found in semantic parsing. We then train a sequence-to-sequence recurrent network (RNN) model with a novel attention-based copying mechanism on datapoints sam- pled from this grammar, thereby teaching the model about these structural proper- ties. Data recombination improves the ac- curacy of our RNN model on three se- mantic parsing datasets, leading to new state-of-the-art performance on the stan- dard GeoQuery dataset for models with comparable supervision. 1 Introduction Semantic parsing—the precise translation of nat- ural language utterances into logical forms—has many applications, including question answer- ing (Zelle and Mooney, 1996; Zettlemoyer and Collins, 2005; Zettlemoyer and Collins, 2007; Liang et al., 2011; Berant et al., 2013), instruc- tion following (Artzi and Zettlemoyer, 2013b), and regular expression generation (Kushman and Barzilay, 2013). Modern semantic parsers (Artzi and Zettlemoyer, 2013a; Berant et al., 2013) are complex pieces of software, requiring hand- crafted features, lexicons, and grammars. Meanwhile, recurrent neural networks (RNNs) what are the major cities in utah ? what states border maine ? Original Examples Train Model Sequence-to-sequence RNN Sample New Examples Synchronous CFG Induce Grammar what are the major cities in [states border [maine]] ? what are the major cities in [states border [utah]] ? what states border [states border [maine]] ? what states border [states border [utah]] ? Recombinant Examples Figure 1: An overview of our system. Given a dataset, we induce a high-precision synchronous context-free grammar. We then sample from this grammar to generate new “recombinant” exam- ples, which we use to train a sequence-to-sequence RNN. have made swift inroads into many structured pre- diction tasks in NLP, including machine trans- lation (Sutskever et al., 2014; Bahdanau et al., 2014) and syntactic parsing (Vinyals et al., 2015b; Dyer et al., 2015). Because RNNs make very few domain-specific assumptions, they have the poten- tial to succeed at a wide variety of tasks with min- imal feature engineering. However, this flexibil- ity also puts RNNs at a disadvantage compared to standard semantic parsers, which can generalize naturally by leveraging their built-in awareness of logical compositionality. In this paper, we introduce data recombina- tion, a generic framework for declaratively inject- 12 GEO x: “what is the population of iowa ?” y: _answer ( NV , ( _population ( NV , V1 ) , _const ( ATISV0 , _stateid ( iowa ) ) ) ) x: “can you list …

Content truncated to 500 words