Online Learning of Relaxed CCG Grammars for Parsing to Logical Form

By Luke S. Zettlemoyer, Michael Collins. 2007

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Citations in the corpus

SPoC: Search-based Pseudocode to Code (2019, background): “Previous work on executable semantic parsing mainly focuses on translating short text descriptions to a one-line program [42, 35, 43, 44, 21, 8], and while recent work explored generating longer programs from text descriptions [22, 40, 29, 15, 16, 14], these programs are mostly evaluated on…”
MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms (2019, background): “Learning logical forms has led to success in other areas of semantic parsing (Cheng et al., 2017; Zelle and Mooney, 1996; Zettlemoyer and Collins, 2007, 2005) and is a natural representation for math word problem-solving steps.”
Joint Concept Learning and Semantic Parsing from Natural Language Explanations (2017, methodology): “tional training of a semantic parser using complete annotations of statements with their logical forms (Zettlemoyer and Collins, 2007).”
Learning Dependency-Based Compositional Semantics (2011, background): “Since then, there has been a healthy line of work yielding increasingly more accurate semantic parsers by using new semantic representations and machine learning techniques (Zelle and Mooney, 1996; Miller et al., 1996; Tang and Mooney, 2001; Ge and Mooney, 2005; Kate et al., 2005; Zettlemoyer and Co”
A Multi-World Approach to Question Answering about Real-World Scenes based on Uncertain Input (2014, positive): “Although the architecture learns the mapping from weak supervision, it achieves comparable results to the semantic parsers that rely on manual annotations of logical forms ([2], [3]).”
Weakly Supervised Learning of Semantic Parsers for Mapping Instructions to Actions (2013, methodology): “Unlike previous work (Zettlemoyer & Collins, 2005, 2007; Artzi & Zettlemoyer, 2011), we have no access to a set of logical constants, either through the the labeled logical form or the weak supervision signal, to guide the GENLEX procedure.”
Compositional Semantic Parsing on Semi-Structured Tables (2015, background): “, 2011) usually cover a smaller scope of operations and compositionality, while early statistical semantic parsers for question answering (Wong and Mooney, 2007; Zettlemoyer and Collins, 2007) and high-accuracy natural language interfaces for databases (Androutsopoulos et al.”
Learning Semantic Correspondences with Less Supervision (2009, background): “Recent work in learning semantics has focused on mapping sentences to meaning representations (e.g., some logical form) given aligned sentence/meaning pairs as training data (Ge and Mooney, 2005; Zettlemoyer and Collins, 2005; Zettlemoyer and Collins, 2007; Lu et al., 2008).”
Data Recombination for Neural Semantic Parsing (2016, methodology): “…language utterances into logical forms—has many applications, including question answering (Zelle and Mooney, 1996; Zettlemoyer and Collins, 2005; Zettlemoyer and Collins, 2007; Liang et al., 2011; Berant et al., 2013), instruction following (Artzi and Zettlemoyer, 2013b), and regular expression…”
Scaling Semantic Parsers with On-the-Fly Ontology Matching (2013, methodology): “We build on a number of existing algorithmic ideas, including using CCGs to build meaning representations (Zettlemoyer and Collins, 2005, 2007; Kwiatkowski et al., 2010, 2011), building derivations to transform the output of the CCG parser based on context (Zettlemoyer and Collins, 2009), and using…”
Language to Logical Form with Neural Attention (2016, neutral): “Tables 2–4 present comparisons against a variety of systems previously described Method Accuracy SCISSOR (Ge and Mooney, 2005) 72.3 KRISP (Kate and Mooney, 2006) 71.7 WASP (Wong and Mooney, 2006) 74.8 λ -WASP (Wong and Mooney, 2007) 86.6 LNLZ08 (Lu et al., 2008) 81.8 ZC05 (Zettlemoyer and Collins, 2”
Learning Language Games through Interaction (2016, methodology): “While online gradient descent is frequently used, for example in semantic parsing (Zettlemoyer and Collins, 2007; Chen, 2012), we using it in a truly online setting, taking one pass over the data and measuring online accuracy (Cesa-Bianchi and Lugosi, 2006).”
Automatically Solving Number Word Problems by Semantic Parsing and Reasoning (2015, background): “Another type of semantic parsing work (Zelle & Mooney, 1996; Zettlemoyer & Collins, 2005; Zettlemoyer & Collins, 2007; Wong & Mooney, 2007; Cai & Yates, 2013; Berant et al., 2013; Kwiatkowski et al., 2013; Berant & Liang, 2014) maps NL text into logical forms by supervised or semi-supervised…”
Abstract Syntax Networks for Code Generation and Semantic Parsing (2017, background): “This difference may be partially attributable to the use of typing information or rich lexicons in most previous semantic parsing approaches (Zettlemoyer and Collins, 2007; Kwiatkowski et al., 2013; Wang et al., 2014; Zhao and Huang, 2015).”
Information Extraction over Structured Data: Question Answering with Freebase (2014, background): “Most work approaches this via the bridge of various intermediate representations, including combinatory categorial grammar (Zettlemoyer and Collins, 2005, 2007, 2009; Kwiatkowski et al., 2010, 2011, 2013), synchronous context-free grammars (Wong and Mooney, 2007), dependency trees (Liang et al.,…”
Towards a Visual Turing Challenge (2014, background): “Recently we witness a tremendous progress in the machine perception [1, 2, 3, 4, 5, 6, 7, 8] and in the language understanding [9, 10, 11, 12, 13] tasks.”