ENGLISH AS A FORMAL LANGUAGE

By Richard Montague. 1975

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ShapeWorld - A new test methodology for multimodal language understanding (2017, background): “In fact, our generation system closely resembles classical work in formal semantics, where a statement corresponds to a logical expression which can be evaluated against an abstract world model (Montague, 1970).”
SELFEXPLAIN: A Self-Explaining Architecture for Neural Text Classifiers (2021, background): “Since natural language is highly compositional (Montague, 1970), it is essential that interpreting a text sequence goes beyond individual words.”
Experimental Support for a Categorical Compositional Distributional Model of Meaning (2011, background): “Compositional type-logical approaches (Montague, 1974; Lambek, 2008) and distributional models of lexical semantics (Schutze, 1998; Firth, 1957) have provided two partial orthogonal solutions to the question.”
Multi-Step Regression Learning for Compositional Distributional Semantics (2013, methodology): “In formal semantics, composition has always been modeled in terms of function application, treating certain words as functions that operate on other words to construct meaning incrementally according to a calculus of composition that reflects the syntactic structure of sentences (Frege, 1892; Montag”
RNNs Implicitly Implement Tensor Product Representations (2018, unknown): “Compositional symbolic representations are widely held to be necessary for intelligence (Newell, 1980; Fodor & Pylyshyn, 1988), particularly in the domain of language (Montague, 1974).”
Composition in Distributional Models of Semantics (2010, background): “Composition operations can be naturally accounted for within logic-based semantic frameworks (Montague, 1974).”
COGS: A Compositional Generalization Challenge Based on Semantic Interpretation (2020, background): “Humans can produce and understand linguistic expressions that they have not encountered before, by systematically combining atomic building blocks (Montague, 1974).”
Boys buying two sausages each: On the syntax and semantics of distance-distributivity (2002, background): “Montague (1970a) was the first to show that natural language expressions can be treated like formal languages, and that an intermediate translation into a formal language is therefore unnecessary.(15) Montague provided the necessary tools for interpreting natural language expressions directly, while”
The Semantic Classification of Adjectives. A View from Syntax. (2014, background): “He was a good electrical engineer b. *He was an eléctrical good engineer 14 Published as Clark (1970), Kamp (1975), Parsons (1970) and Montague (1970). all, even though vague and context dependent.”
Semantic Compositionality through Recursive Matrix-Vector Spaces (2012, background): “Answering this question can also be seen as a ﬁrst step towards bridging the gap between logic-based, formal semantics (Montague, 1974) and vector space models.”
“Not not bad” is not “bad”: A distributional account of negation (2013, background): “Coecke et al. (2010) postulate a mathematical framework generalising the syntax-semantic passage of Montague Grammar (Montague, 1974) to other forms of syntactic and semantic representation.”
A Theory of Truth and Semantic Representation (2008, background): “In presenting the syntax I shall presume some familiarity with Montague grammar, specifically with Montague (1970a) and (1973).”
Vector-based Models of Semantic Composition (2008, background): “Computational models of semantics which use symbolic logic representations (Montague, 1974) can account naturally for the meaning of phrases or sentences.”
Connectionist sentence processing in perspective (1999, background): “As in the work of Montague (1970, 1974), it is possible in principle to regard logical form as no more than the flow of control in computing models, in the sense of that term used in model-theoretic semantics.”
Recursive deep learning for natural language processing and computer vision (2014, background): “Answering this question can also be seen as a first step towards bridging the gap between logic-based, formal semantics (Montague, 1974) and vector space models.”