Neural Generation of Regular Expressions from Natural Language with
            Minimal Domain Knowledge

Locascio, Nicholas; Narasimhan, Karthik; DeLeon, Eduardo; Kushman, Nate; Barzilay, Regina

doi:10.18653/v1/d16-1197

Cited by 68 publications

(120 citation statements)

References 4 publications

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“…Another approach is to treat semantic parsing as a machine translation problem, where the logical form is linearized then predicted as an unstructured sequence of tokens (Andreas et al, 2013). This approach is taken by recent neural semantic parsers (Jia and Liang, 2016;Dong and Lapata, 2016;Locascio et al, 2016;Ling et al, 2016). This approach has the advantage of predicting the logical form directly from the question without latent variables, which simplifies learning, but the disadvantage of ignoring type constraints on logical forms.…”

Section: Related Workmentioning

confidence: 99%

Neural Semantic Parsing with Type Constraints for Semi-Structured Tables

Krishnamurthy¹,

Dasigi²,

Gardner³

2017

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

201

156

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We present a new semantic parsing model for answering compositional questions on semi-structured Wikipedia tables. Our parser is an encoder-decoder neural network with two key technical innovations: (1) a grammar for the decoder that only generates well-typed logical forms; and (2) an entity embedding and linking module that identifies entity mentions while generalizing across tables. We also introduce a novel method for training our neural model with question-answer supervision. On the WIKITABLEQUESTIONS data set, our parser achieves a state-of-theart accuracy of 43.3% for a single model and 45.9% for a 5-model ensemble, improving on the best prior score of 38.7% set by a 15-model ensemble. These results suggest that type constraints and entity linking are valuable components to incorporate in neural semantic parsers.

show abstract

Section: Related Workmentioning

confidence: 99%

Neural Semantic Parsing with Type Constraints for Semi-Structured Tables

Krishnamurthy¹,

Dasigi²,

Gardner³

2017

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

201

156

View full text Add to dashboard Cite

show abstract

“…There is significant existing research on mapping NL directly to executable programs in the form of logical forms (Zettlemoyer and Collins, 2005), λ-DCS (Liang et al, 2013), regular expressions (Kushman and Barzilay, 2013;Locascio et al, 2016), database queries (Iyer et al, 2017;Zhong et al, 2017) and general purpose programs (Balog et al, 2016;Allamanis et al, 2015b). Ling et al (2016) Gu et al (2016b) use neural models to map NL queries to a sequence of API calls, and Neelakantan et al (2015) augment neural models with a small set of basic arithmetic and logic operations to generate more meaningful programs.…”

Section: Related Workmentioning

confidence: 99%

Mapping Language to Code in Programmatic Context

Iyer¹,

Konstas²,

Cheung³

et al. 2018

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

110

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Source code is rarely written in isolation. It depends significantly on the programmatic context, such as the class that the code would reside in. To study this phenomenon, we introduce the task of generating class member functions given English documentation and the programmatic context provided by the rest of the class. This task is challenging because the desired code can vary greatly depending on the functionality the class provides (e.g., a sort function may or may not be available when we are asked to "return the smallest element" in a particular member variable list). We introduce CONCODE, a new large dataset with over 100,000 examples consisting of Java classes from online code repositories, and develop a new encoder-decoder architecture that models the interaction between the method documentation and the class environment. We also present a detailed error analysis suggesting that there is significant room for future work on this task.

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“…RE (Locascio et al, 2016). By contrast, our work aims to use REs to improve the prediction ability of a NN.…”

Section: Related Workmentioning

confidence: 99%

Marrying Up Regular Expressions with Neural Networks: A Case Study for Spoken Language Understanding

Luo¹,

Feng²,

Wang³

et al. 2018

Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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The success of many natural language processing (NLP) tasks is bound by the number and quality of annotated data, but there is often a shortage of such training data. In this paper, we ask the question: "Can we combine a neural network (NN) with regular expressions (RE) to improve supervised learning for NLP?". In answer, we develop novel methods to exploit the rich expressiveness of REs at different levels within a NN, showing that the combination significantly enhances the learning effectiveness when a small number of training examples are available. We evaluate our approach by applying it to spoken language understanding for intent detection and slot filling. Experimental results show that our approach is highly effective in exploiting the available training data, giving a clear boost to the RE-unaware NN. flights from Boston to Miami Intent RE: Intent Label: flight /from (__CITY) to (__CITY)/ O O B-fromloc.city O B-toloc.city Sentence: Slot Labels: Slot RE: /^flights? from/ REtag: flight city / toloc.city REtag: city / fromloc.city

show abstract

Neural Generation of Regular Expressions from Natural Language with Minimal Domain Knowledge

Cited by 68 publications

References 4 publications

Neural Semantic Parsing with Type Constraints for Semi-Structured Tables

Neural Semantic Parsing with Type Constraints for Semi-Structured Tables

Mapping Language to Code in Programmatic Context

Marrying Up Regular Expressions with Neural Networks: A Case Study for Spoken Language Understanding

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