Weakly Supervised Training of Semantic Parsers

By Jayant Krishnamurthy, Tom Michael Mitchell. 2012

157 citations recorded by the source corpus.

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In Natural Language Processing

Citations in the corpus

Semantic Parsing on Freebase from Question-Answer Pairs (2013, methodology): “Previous work based on CCG requires manually specifying combination rules (Krishnamurthy and Mitchell, 2012) or inducing the rules from annotated logical forms (Kwiatkowski et al.”
Joint Concept Learning and Semantic Parsing from Natural Language Explanations (2017, methodology): “Following recent work in semantic parsing (Liang and Potts, 2015; Krishnamurthy and Mitchell, 2012), we use a log-linear model over logical forms: where φ ( s, l ) ∈ R d is a feature vector over statements s and logical interpretations l .”
The Web as a Knowledge-Base for Answering Complex Questions (2018, background): “Conversely, work on QA through semantic parsing has focused primarily on compositionality: questions are translated to compositional programs that encode a sequence of actions for finding the answer in a knowledge-base (KB) (Zelle and Mooney, 1996; Zettlemoyer and Collins, 2005; Artzi and Zettlemoye”
A Multi-World Approach to Question Answering about Real-World Scenes based on Uncertain Input (2014, background): “Single-world approach for question answering problem We build on recent progress on endto-end question answering systems that are solely trained on question-answer pairs (Q,A) [1, 13].”
Weakly Supervised Learning of Semantic Parsers for Mapping Instructions to Actions (2013, background): “…(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 conversational logs and…”
Scaling Semantic Parsers with On-the-Fly Ontology Matching (2013, background): “, 2011), relation extraction (Krishnamurthy and Mitchell, 2012), robot control (Matuszek et al.”
Jointly Learning to Parse and Perceive: Connecting Natural Language to the Physical World (2013, background): “…can be trained using sentences annotated with their formal representation (Zelle and Mooney, 1996; Zettlemoyer and Collins, 2005; Kate and Mooney, 2006; Kwiatkowski et al., 2010) or various less restrictive annotations (Clarke et al., 2010; Liang et al., 2011; Krishnamurthy and Mitchell, 2012).”
Information Extraction over Structured Data: Question Answering with Freebase (2014, background): “More recent research started to minimize this direct supervision by using latent meaning representations (Berant et 2https://code.google.com/p/jacana al., 2013; Kwiatkowski et al., 2013) or distant supervision (Krishnamurthy and Mitchell, 2012).”
Question Answering over Freebase with Multi-Column Convolutional Neural Networks (2015, background): “Recent works mainly focus on using question-answer pairs, instead of annotated logical forms of questions, as weak training signals (Liang et al., 2011; Krishnamurthy and Mitchell, 2012) to reduce annotation costs.”
Learning a Neural Semantic Parser from User Feedback (2017, methodology): “We used a minibatch size of 100 and used Adam (Kingma and Ba, 2015) with a learning rate of 0.001 for 70 epochs for all our experiments.”
Neural Semantic Parsing with Type Constraints for Semi-Structured Tables (2017, neutral): “…lexicalized grammar formalisms such as Combinatory Categorial Grammar (Zettlemoyer 1516 and Collins, 2005, 2007; Kwiatkowski et al., 2011, 2013; Krishnamurthy and Mitchell, 2012; Artzi and Zettlemoyer, 2013) and others (Liang et al., 2011; Berant et al., 2013; Zhao and Huang, 2015; Wong and…”
Learning executable semantic parsers for natural language understanding (2016, background): “One can use a large corpus of text to exploit even weaker supervision [16, 27].”
From Language to Programs: Bridging Reinforcement Learning and Maximum Marginal Likelihood (2017, neutral): “put (Clarke et al., 2010; Liang et al., 2011; Krishnamurthy and Mitchell, 2012; Artzi and Zettlemoyer, 2013; Liang et al., 2017).”
Large-scale Semantic Parsing without Question-Answer Pairs (2014, background): “2011; Artzi and Zettlemoyer, 2013), via distant supervision (Krishnamurthy and Mitchell, 2012; Cai and Yates, 2013), from questions (Goldwasser et al., 2011; Poon, 2013; Fader et al., 2013), and question-answer pairs (Clarke et al., 2010; Liang et al., 2011).”
Scalable Semantic Parsing with Partial Ontologies (2015, unknown): “Krishnamurthy and Mitchell (2015)”
Vision-Based Navigation With Language-Based Assistance via Imitation Learning With Indirect Intervention (2018, background): “Proposals include a variety of grounded parsing models that are trained from data [41, 42, 36] and models that interact with robots via natural language queries against a knowledge base [52] Most relevant to the present work are [44] who ground natural language to robotic manipulator instructions us”
Toward Interactive Grounded Language Acqusition (2013, background): “More relevant to this work are weakly-supervised formulations [5, 18, 15], which observe functions of the output semantic parse.”
Sequence-to-Action: End-to-End Semantic Graph Generation for Semantic Parsing (2018, background): “Semantic parsing has received significant attention for a long time (Kate and Mooney, 2006; Clarke et al., 2010; Krishnamurthy and Mitchell, 2012; Artzi and Zettlemoyer, 2013; Berant and Liang, 2014; Quirk et al., 2015; Artzi et al., 2015; Reddy et al., 2017).”
Learning an Executable Neural Semantic Parser (2017, background): “sing has attracted a great deal of attention due to its utility in a wide range of applications such as question answering (Kwiatkowski et al. 2011;Liang, Jordan, and Klein2011), relation extraction (Krishnamurthy and Mitchell 2012), goal-oriented dialog (Wen et al.2015), natural language interfaces”