Learning to Parse Database Queries Using Inductive Logic Programming

By J. Zelle, Raymond J. Mooney. 1996

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In Natural Language Processing

Citations in the corpus

DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs (2019, background): “Semantic parsing: The semantic parsing literature has a long history of trying to understand complex, compositional question semantics in terms of some grounded knowledge base or other environment (Zelle and Mooney, 1996; Zettlemoyer and Collins, 2005; Berant et al., 2013a, inter alia ).”
Semantic Parsing on Freebase from Question-Answer Pairs (2013, background): “Traditional semantic parsers (Zelle and Mooney, 1996; Zettlemoyer and Collins, 2005; Wong and Mooney, 2007; Kwiatkowski et al., 2010) have two limitations: (i) they require annotated logical forms as supervision, and (ii) they operate in limited domains with a small number of logical predicates.”
Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021, neutral): “Wu et al. (2021) is an example of an approach in this spirit.”
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.”
InCoder: A Generative Model for Code Infilling and Synthesis (2022, background): “, input-output examples [12, 28, 19, 13], partial implementations [58], or natural language descriptions [74, 73, 72, 42, 18] of the desired program as input.”
Break It Down: A Question Understanding Benchmark (2020, methodology): “…Collection Questions in BREAK were randomly sampled from ten QA datasets over the following tasks (Table 3): • Semantic Parsing: Mapping natural language utterances into formal queries, to be executed on a target KB (Price, 1990; Zelle and Mooney, 1996; Li and Jagadish, 2014; Yu et al., 2018).”
MCTest: A Challenge Dataset for the Open-Domain Machine Comprehension of Text (2013, background): “…880 geographical facts about the US and has played a similar role for written (as opposed to spoken) natural language queries against a database (Zelle and Mooney, 1996) and it also continues to spur research (see for example Goldwasser et al., 2011), as does the similar Jobs database, which…”
Training Classifiers with Natural Language Explanations (2018, methodology): “Semantic parsing of natural language into logical forms is recognized as a challenging problem and has been studied extensively (Zelle and Mooney, 1996; Zettlemoyer and Collins, 2005; Liang et al., 2011; Liang, 2016).”
A simple neural network module for relational reasoning (2017, background): “ts on CLEVR [15] than [14], and on par with strongly supervised system of [16]. Text-based question answering Answering text-based questions has long been an active research area in the NLP community [3, 22, 27, 48]. Recently, in addition to traditional symbolic-based question answering architecture”
Parsing Algebraic Word Problems into Equations (2015, background): “Our work is related to situated semantic interpretation, which aims to map natural language sentences to formal meaning representations (Zelle and Mooney, 1996; Zettlemoyer and Collins, 2005; Ge and Mooney, 2006; Kwiatkowski et al., 2010).”
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning (2017, background): “In semantic parsing for question answering (QA), natural language questions are parsed into logical forms that are then executed on a knowledge graph (Zelle & Mooney, 1996; Wong & Mooney, 2007; Zettlemoyer & Collins, 2005; 2007).”
UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models (2022, background): “• Semantic parsing converts questions to logical forms (Zelle and Mooney, 1996; Zettlemoyer and Collins, 2005).”
DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation (2022, background): “Zelle & Mooney (1996) and Zettlemoyer & Collins (2007) At the same time, however, it becomes more difficult to build reliable benchmarks aligned with real-world applications.”
Modifying Memories in Transformer Models (2020, methodology): “as compared to those Transformer models that solely rely on implicit memorization. 2 Related Works KBs are widely utilized to store and access the relational knowledge in NLP domain (Ji et al., 2020; Zelle and Mooney, 1996; Zettlemoyer and Collins, 2005, inter alia). However, the recent success of 2”
Learning to Map Sentences to Logical Form: Structured Classification with Probabilistic Categorial Grammars (2005, methodology): “Previous work by Thompson and Mooney (2002) and Zelle and Mooney (1996) used a subset of the Geo880 corpus.”
Learning to Automatically Solve Algebra Word Problems (2014, background): “Other approaches have relied on access to more costly annotated logical forms (Zelle and Mooney, 1996; Thompson and Mooney, 2003; Wong and Mooney, 2006; Zettle-moyer and Collins, 2005; Kwiatkowski et al., 2010).”
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”
Learning Dependency-Based Compositional Semantics (2011, background): “In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. law.”