Lambda Dependency-Based Compositional Semantics

By Percy Liang. 2013

145 citations recorded by the source corpus.

This short note presents a new formal language, lambda dependency-based compositional semantics (lambda DCS) for representing logical forms in semantic parsing. By eliminating variables and making existential quantification implicit, lambda DCS logical forms are generally more compact than those in lambda calculus.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Semantic Parsing on Freebase from Question-Answer Pairs (2013, background): “x), but as these constructions are not central to this paper, we defer to (Liang, 2013).”
Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks (2015, methodology): “In terms of diversity, some of our tasks are related to existing setups but we also propose many additional ones; tasks 3.8 and 3.9 are inspired by previous work on lambda dependency-based compositional semantics (Liang et al., 2013; Liang, 2013) for instance.”
The Web as a Knowledge-Base for Answering Complex Questions (2018, methodology): “This is similar to function composition in CCG (Steedman, 2000), or a join operation in λ-DCS (Liang, 2013), where the string is a function applied to previously-computed values.”
Compositional Semantic Parsing on Semi-Structured Tables (2015, methodology): “As our language for logical forms, we use lambda dependency-based compositional semantics (Liang, 2013), or lambda DCS, which we briefly describe here.”
Semantic Parsing via Paraphrasing (2014, background): “For a formal description of simple λ -DCS, see Liang (2013) and Berant et al. (2013).”
Building a Semantic Parser Overnight (2015, methodology): “Here we give a brief description; see Liang (2013) for details.”
Semantic Parsing via Staged Query Graph Generation: Question Answering with Knowledge Base (2015, methodology): “straightforwardly mapped to a logical form in λcalculus and is semantically closely related to λDCS (Liang, 2013).”
Learning executable semantic parsers for natural language understanding (2016, background): “Another logical language, which can be viewed as syntactic sugar for lambda calculus, is lambda dependency-based semantics (DCS) [20].”
Naturalizing a Programming Language via Interactive Learning (2017, background): “Actions usually take sets as arguments, which are represented using lambda dependency-based compositional semantics (lambda DCS) expressions (Liang, 2013).”
Beyond I.I.D.: Three Levels of Generalization for Question Answering on Knowledge Bases (2020, methodology): “We find that S-expression provides a good trade-off on compactness, compositionality, and readability, but one may choose any other formalism for linearization such as λ-DCS [26] or directly use the corresponding SPARQL queries.”
Learning Structured Natural Language Representations for Semantic Parsing (2017, methodology): “A more compact logical formulation which our method also applies to is λ-DCS (Liang, 2013).”
Statistical learning for semantic parsing: A survey (2019, methodology): “An alternative approach which is less complicated than CCG is to adopt a cruder grammar whose rules correspond directly to the lambda Dependency-based Composition Semantics ( lambda DCS ) [33] , join and intersect.”
Neural, symbolic and neural-symbolic reasoning on knowledge graphs (2021, background): “Berant et al. (2013) follow Lambda Dependency-based Compositional Semantics ( λ -DCS) (Liang, 2013) to transform the questions, in order to make existential quanti ﬁ cation implicit, thus reducing the number of variables.”
Knowledge Graph and Corpus Driven Segmentation and Answer Inference for Telegraphic Entity-seeking Queries (2014, background): “…connective relations can be inferred via semantic parsing (Kasneci et al., 2008; Pound et al., 2012; Yahya et al., 2012; Berant et al., 2013; Kwiatkowski et al., 2013) and translated to lambda expressions (Liang, 2013) or SPARQL style queries (Kasneci et al., 2008), with elaborate schema knowledge.”
Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation (2019, methodology): “Inspired by lambda DCS (Liang, 2013), SemQL is designed to be tree-structured.”
Benchmarking Meaning Representations in Neural Semantic Parsing (2020, background): “Except for the four MRs that we study, Liang et al. (2011); Liang (2013) presented DCS and lambda DCS for querying knowledge bases and demonstrated their advantages over Lambda Calculus.”
A Survey on Semantic Parsing (2018, background): “Age.20 in λ -DCS. Readers are directed to [Liang, 2013] for details.”
Simpler Context-Dependent Logical Forms via Model Projections (2016, methodology): “More recent work learns directly from denotations (Clarke et al., 2010; Liang, 2013; Berant et al., 2013; Artzi and Zettlemoyer, 2013), but in this setting, a constant struggle is to contain the exponential explosion of possible logical forms.”
RNG-KBQA: Generation Augmented Iterative Ranking for Knowledge Base Question Answering (2021, background): “an executable logical form in various representations, including lambda-DCS (Liang, 2013; Berant et al., 2013), sparql query (Das et al.”
Semantic Evaluation for Text-to-SQL with Distilled Test Suite (2020, background): “Since these two conditions hold in many execution environments, our framework might potentially be applied to other logical forms, such as λ-DCS (Liang, 2013), knowledge graphs (Lin et al.”