Inducing Tree-Substitution Grammars

By Trevor Cohn, Phil Blunsom, Sharon Goldwater. 2010

110 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

A Survey of Machine Learning for Big Code and Naturalness (2017, background): “ion tree programs.Similarly, Wang et al.[185], Yin and Neubig [196] use an LSTM over AST nodes to achieve the same goal. Allamanis and Sutton [12] also create a syntactic model learning Bayesian TSGs [43, 156] (see Section 4.3). Semantic Models(graphs). Semantic code-generatingmodelsview codeasagrap”
Mining idioms from source code (2014, methodology): “. But, as we have seen, simple methods of formalizing this intuition do not work (see section 2), we resort to methods that are not as simple. We apply recent advanced techniques from statistical NLP [9, 40], but we need to explain them in some detail to justify why they are appropriate for this sof”
A Survey on Large Language Models for Code Generation (2024, unknown): “The accessibility, effectiveness, and efficiency of using LLMs in daily tasks show the huge potential of applying LLMs in automatic code generation.”
Program Synthesis and Semantic Parsing with Learned Code Idioms (2019, background): “A pTSG is a probabilistic context-free grammar extended with production rules that expand to a whole AST fragment instead of a single level of symbols [8, 29].”
Stylometric Analysis of Scientific Articles (2012, methodology): “Efﬁcient Bayesian procedures have recently been developed that enable the training of large-scale probabilistic TSG grammars (Post and Gildea, 2009; Cohn et al., 2010).”
Bootstrapping language acquisition. (2017, background): “…syntactic structures such as dependency or constituency structure, and even syntactic categories, have been notably less successful despite considerable effort (e.g. Abend, Reichart, & Rappoport, 2010; Christodoulopoulos, Goldwater, & Steedman, 2010; Cohn et al., 2010; Klein & Manning, 2004, 2005).”
Bayesian Symbol-Refined Tree Substitution Grammars for Syntactic Parsing (2012, methodology): “In recent years, there has been an increasing interest in tree substitution grammar (TSG) as an alternative to CFG for modeling syntax trees (Post and Gildea, 2009; Tenenbaum et al., 2009; Cohn et al., 2010).”
Distribution Augmentation for Low-Resource Expressive Text-To-Speech (2022, unknown): “constituency parse), these formulations allow estimating a generative grammar of the language [20].”
From Natural Language Specifications to Program Input Parsers (2013, unknown): “This sampling algorithm with a changing proposal distribution has been shown to work well in practice (Johnson and Griffiths, 2007; Cohn et al., 2010; Naseem and Barzilay, 2011).”