Driving Semantic Parsing from the World’s Response

By James Clarke, Dan Goldwasser, Ming-Wei Chang, Dan Roth. 2010

263 citations recorded by the source corpus.

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Citations in the corpus

Semantic Parsing on Freebase from Question-Answer Pairs (2013, methodology): “Besides semantic parsing for querying databases (Popescu et al., 2003; Clarke et al., 2010; Liang et al., 2011), previous work has looked at interpreting natural language for performing programming tasks (Kushman and Barzilay, 2013; Lei et al.”
Latent Retrieval for Weakly Supervised Open Domain Question Answering (2019, background): “Our approach is also reminiscent of weakly supervised semantic parsing (Clarke et al., 2010; Liang et al., 2013; Artzi and Zettlemoyer, 2013; Fader et al., 2014; Berant et al., 2013; Kwiatkowski et al., 2013), with which we share similar challenges—(1) inference and learning are tightly coupled,…”
Break It Down: A Question Understanding Benchmark (2020, methodology): “Our formalism, Question Decomposition Meaning Representation (QDMR), is inspired by database query languages (SQL; SPARQL), and by semantic parsing (Zelle and Mooney, 1996; Zettlemoyer and Collins, 2005; Clarke et al., 2010), in which questions are given full meaning representations.”
A Discrete Hard EM Approach for Weakly Supervised Question Answering (2019, background): “Latent-variable learning has been extensively studied in the literature of semantic parsing (Zettlemoyer and Collins, 2005; Clarke et al., 2010; Liang et al., 2013; Berant et al., 2013; Artzi and Zettlemoyer, 2013).”
Training Classifiers with Natural Language Explanations (2018, background): “One notable trend is to learn semantic parsers from weak supervision (Clarke et al., 2010; Liang et al., 2011), whereas our goal is to obtain weak supervision signal from semantic parsers.”
Joint Concept Learning and Semantic Parsing from Natural Language Explanations (2017, background): “Our work is also closely related to work by Goldwasser and Roth (2014); Clarke et al. (2010), who also train semantic parsers in weakly supervised contexts, where language interpretation is integrated in real-world tasks.”
Dialog-based Language Learning (2016, background): “There is work on the the use of feedback and dialog for learning, notably for collecting knowledge to answer questions [8, 17], the use of natural language instruction for learning symbolic rules [10, 5] and the use of binary feedback (rewards) for learning parsers [3].”
Learning to Automatically Solve Algebra Word Problems (2014, background): “Examples include question answering (Clarke et al., 2010; Cai and Yates, 2013a; Cai and Yates, 2013b; Berant et al., 2013; Kwiatkowski et al., 2013), dialog systems (Artzi and Zettlemoyer, 2011), robot instruction (Chen and Mooney, 2011; Chen, 2012; Kim and Mooney, 2012; Matuszek et al., 2012;…”
Learning Dependency-Based Compositional Semantics (2011, methodology): “We first compare our system (henceforth, LJK11) with Clarke et al. (2010) (henceforth, CGCR10), which is most similar to our work in that it also learns from question–answer pairs without using annotated logical forms.”
Weakly Supervised Learning of Semantic Parsers for Mapping Instructions to Actions (2013, methodology): “Clarke et al. (2010), Goldwasser and Roth (2011) and Liang et al. (2011) describe approaches for learning semantic parsers from sentences paired with responses, Krishnamurthy and Mitchell (2012) describe using distant supervision, Artzi and Zettlemoyer (2011) use weak supervision from…”
Neural Programmer: Inducing Latent Programs with Gradient Descent (2015, background): “Methods such as Piantadosi et al. (2008); Eisenstein et al. (2009); Clarke et al. (2010) learn a compositional semantic model without hand engineered compositional grammar, but still requiring a hand labeled lexical mapping of words to the operations.”
A Joint Model of Language and Perception for Grounded Attribute Learning (2012, background): “Clarke (2010) and Liang (2011) describe approaches to learning semantic parsers from questions paired with database answers, while Gold-wasser (2011) presents work on unsupervised learning.”
Data Recombination for Neural Semantic Parsing (2016, background): “In the last few years, there has an emergence of semantic parsers learned from de-notations (Clarke et al., 2010; Liang et al., 2011; Berant et al., 2013; Artzi and Zettlemoyer, 2013b).”
Modeling Biological Processes for Reading Comprehension (2014, background): “In recent years, several lines of work addressed semantic parsing using various formalisms and levels of supervision (Zettle-moyer and Collins, 2005; Wong and Mooney, 2006; Clarke et al., 2010; Berant et al., 2013).”
Scaling Semantic Parsers with On-the-Fly Ontology Matching (2013, background): “Recently, alternative forms of supervision were introduced, including learning from question-answer pairs (Clarke et al., 2010; Liang et al., 2011), from conversational logs (Artzi and Zettlemoyer, 2011), with distant supervision (Krishnamurthy and Mitchell, 2012; Cai and Yates, 2013b), and from sen”
Jointly Learning to Parse and Perceive: Connecting Natural Language to the Physical World (2013, background): “, 2010) or various less restrictive annotations (Clarke et al., 2010; Liang et al., 2011; Krishnamurthy and Mitchell, 2012).”
Language to Logical Form with Neural Attention (2016, background): “…and Zettlemoyer, 2011), system demonstrations (Chen and Mooney, 2011; Goldwasser and Roth, 2011; Artzi and Zettle-moyer, 2013), question-answer pairs (Clarke et al., 2010; Liang et al., 2013), and distant supervision (Krishnamurthy and Mitchell, 2012; Cai and Yates, 2013; Reddy et al., 2014).”
Building a Semantic Parser Overnight (2015, background): “Recent work has made signiﬁcant strides in reducing supervision from logical forms (Zettle-moyer and Collins, 2005; Wong and Mooney, 2007) to denotations (Clarke et al., 2010; Liang et al., 2011) and to weaker forms (Artzi and Zettle-moyer, 2011; Reddy et al., 2014).”
Neural Module Networks for Reasoning over Text (2019, methodology): “Approaches have used labeled logical-forms (Zelle & Mooney, 1996; Zettlemoyer & Collins, 2005), or weak QA supervision (Clarke et al., 2010; Berant et al., 2013; Reddy et al., 2014) to learn parsers to answer questions against structured knowledge bases.”
Learning to Win by Reading Manuals in a Monte-Carlo Framework (2011, background): “A second line of prior work has focused on full semantic parsing – converting a given text into a formal meaning representation such as first order logic (Clarke et al., 2010).”