Semantic Parsing as Machine Translation

By Jacob Andreas, Andreas Vlachos, Stephen Clark. 2013

99 citations recorded by the source corpus.

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Citations in the corpus

A Decomposable Attention Model for Natural Language Inference (2016, neutral): “, 2008), and semantic parsing (Andreas et al., 2013).”
Program Induction by Rationale Generation: Learning to Solve and Explain Algebraic Word Problems (2017, background): “…Kalchbrenner and Blun-som, 2013), and inherits ideas from the extensive literature on semantic parsing (Jones et al., 2012; Berant et al., 2013; Andreas et al., 2013; Quirk et al., 2015; Liang et al., 2016; Neelakantan et al., 2016) and program generation (Reed and de Fre-itas, 2016; Graves et…”
Learning to Compose Neural Networks for Question Answering (2016, background): “Supervision may be provided either by annotated logical forms (Wong and Mooney, 2007; Kwiatkowski et al., 2010; Andreas et al., 2013) or from (world, question, answer) triples alone (Liang et al.”
Language to Logical Form with Neural Attention (2016, methodology): “…(Kate et al., 2005), classiﬁers based on string kernels (Kate and Mooney, 2006), machine translation (Wong and Mooney, 2006; Wong and Mooney, 2007; Andreas et al., 2013), and combinatory categorial grammar induction techniques (Zettlemoyer and Collins, 2005; Zettle-moyer and Collins, 2007;…”
Learning from Task Descriptions (2020, background): “problem has been well-studied for narrow domains in the semantic parsing literature (Zelle and Mooney, 1996; Zettlemoyer and Collins, 2005; Liang et al., 2011; Andreas et al., 2013), though the input is typically a single static database, not”
Neural Semantic Parsing with Type Constraints for Semi-Structured Tables (2017, background): “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).”
Coarse-to-Fine Decoding for Neural Semantic Parsing (2018, background): “Various models have been proposed over the years to learn semantic parsers from natural language expressions paired with their meaning representations (Tang and Mooney, 2000; Ge and Mooney, 2005; Zettlemoyer and Collins, 2007; Wong and Mooney, 2007; Lu et al., 2008; Kwiatkowski et al., 2011; Andreas”
Language to Code: Learning Semantic Parsers for If-This-Then-That Recipes (2015, methodology): “As a baseline, we adopt a recent approach (Andreas et al., 2013) that casts semantic parsing as phrasal translation.”
A Survey on Semantic Parsing (2018, methodology): “Andreas et al. [2013] explicitly model semantic parsing as a MT task and achieve results comparable to the state of the art on GeoQuery at the time.”
NLmaps: A Natural Language Interface to Query OpenStreetMap (2016, methodology): “The parser employed here is a SMT system that translates from natural language to a machine readable language (MRL), following an approach introduced by Andreas et al. (2013).”
Compositional Generalization and Natural Language Variation: Can a Semantic Parsing Approach Handle Both? (2020, methodology): “NQG is inspired by more traditional approaches to semantic parsing based on grammar formalisms such as CCG (Zettlemoyer and Collins, 2005, 2007; Kwiatkowski et al., 2010, 2013) and SCFG (Wong and Mooney, 2006, 2007; Andreas et al., 2013”
Value-Agnostic Conversational Semantic Parsing (2021, background): “While there was a brief period of interest in using unstructured sequence models for semantic parsing (e.g., Andreas et al., 2013; Dong and Lapata, 2016), most research on semantic parsing has used tree-or graph-shaped decoders that exploit program structure.”
VQA With No Questions-Answers Training (2018, background): “Understanding natural language questions and parsing them to a logical form is a hard problem, still under study [29, 7, 74, 11, 58].”
Building a Neural Semantic Parser from a Domain Ontology (2018, methodology): “For example, both Wong and Mooney (2006) and Andreas et al. (2013) developed word-alignment based translation models for parsing the geoquery dataset.”
Constrained Language Models Yield Few-Shot Semantic Parsers (2021, methodology): “Like the closely related problem of machine translation (Wong and Mooney, 2006; Andreas et al., 2013), semantic parsing has recently been driven by encoder-decoder neural architectures (starting with Dong and Lapata, 2016; Jia and Liang, 2016; Kočiský et al.”
Semantic Parsing with Semi-Supervised Sequential Autoencoders (2016, background): “…logic programming (Zelle and Mooney, 1996), string-to-tree (Galley et al., 2004) and string-to-graph (Jones et al., 2012) transducers, grammar induction (Kwiatkowski et al., 2011; Artzi and Zettlemoyer, 2013; Reddy et al., 2014) or machine translation (Wong and Mooney, 2006; Andreas et al., 2013).”
Learning an Executable Neural Semantic Parser (2017, methodology): “The idea is not novel, because semantic parsing has been previously studied with statistical machine translation approaches in both Wong and Mooney (2006) and Andreas, Vlachos, and Clark (2013).”
A Corpus and Semantic Parser for Multilingual Natural Language Querying of OpenStreetMap (2016, methodology): “The semantic parser we extracted by simple monolingual machine translation (Andreas et al., 2013) achieves a F1 score of 77.3% for answer retrieval on our data.”