Paracompositionality, MWEs and Argument Substitution

Multi-word expressions, verb-particle constructions, idiomatically combining phrases, and phrasal idioms have something in common: not all of their elements contribute to the argument structure of the predicate implicated by the expression. Radically lexicalized theories of grammar that avoid string-, term-, logical form-, and tree-writing, and categorial grammars that avoid wrap operation, make predictions about the categories involved in verb-particles and phrasal idioms. They may require singleton types, which can only substitute for one value, not just for one kind of value. These types are asymmetric: they can be arguments only. They also narrowly constrain the kind of semantic value that can correspond to such syntactic categories. Idiomatically combining phrases do not subcategorize for singleton types, and they exploit another locally computable and compositional property of a correspondence, that every syntactic expression can project its head word. Such MWEs can be seen as empirically realized categorial possibilities, rather than lacuna in a theory of lexicalizable syntactic categories.Comment: accepted version (pre-final) for 23rd Formal Grammar Conference, August 2018, Sofi

Some proof theoretical remarks on quantification in ordinary language

This paper surveys the common approach to quantification and generalised quantification in formal linguistics and philosophy of language. We point out how this general setting departs from empirical linguistic data, and give some hints for a different view based on proof theory, which on many aspects gets closer to the language itself. We stress the importance of Hilbert's oper- ator epsilon and tau for, respectively, existential and universal quantifications. Indeed, these operators help a lot to construct semantic representation close to natural language, in particular with quantified noun phrases as individual terms. We also define guidelines for the design of the proof rules corresponding to generalised quantifiers.Cet article dresse un rapide panorama de l'approche commune de la quantification généralisée ou non en linguistique formelle et en philosophie du langage. Nous montrons que ce cadre général va parfois 'a l'encontre des données linguistiques, et nous donnons quelques indications pour une approche différente basée sur la théorie de la démonstration, qui sur bien des points s'avère plus proche de la langue. Nous soulignons l'importance des opérateurs tau et epsilon de Hilbert, qui rendent respectivement compte de la quantification universelle et existentielle. En effet, ces opérateurs permettent de construire des représentations sémantiques qui suivent la langue avec, en particulier des groupes nominaux quantifiées qui soient des termes individuels. Nous donnons aussi des principes pour définir des règles de déduction qui correspondent aux quantificateurs généralisés

Incremental Semantics for Dialogue Processing: Requirements, and a Comparison of Two Approaches

International audienceTruly interactive dialogue systems need to construct meaning on at least a word-byword basis. We propose desiderata for incremental semantics for dialogue models and systems, a task not heretofore attempted thoroughly. After laying out the desirable properties we illustrate how they are met by current approaches, comparing two incremental semantic processing frameworks: Dynamic Syntax enriched with Type Theory with Records (DS-TTR) and Robust Minimal Recursion Semantics with incremental processing (RMRS-IP). We conclude these approaches are not significantly different with regards to their semantic representation construction, however their purported role within semantic models and dialogue models is where they diverge

Sémantique des déterminants dans un cadre richement typé

International audienceThe variation of word meaning according to the context leads us to enrich the type system of our syntactical and semantic analyser of French based on categorial grammars and Montague semantics (or lambda-DRT). The main advantage of a deep semantic analyse is too represent meaning by logical formulae that can be easily used e.g. for inferences. Determiners and quantifiers play a fundamental role in the construction of those formulae. But in our rich type system the usual semantic terms do not work. We propose a solution ins- pired by the tau and epsilon operators of Hilbert, kinds of generic elements and choice functions. This approach unifies the treatment of the different determi- ners and quantifiers as well as the dynamic binding of pronouns. Above all, this fully computational view fits in well within the wide coverage parser Grail, both from a theoretical and a practical viewpoint.La variation du sens des mots en contexte nous a conduit à enrichir le système de types utilisés dans notre analyse syntaxico-sémantique du français basé sur les grammaires catégorielles et la sémantique de Montague (ou la lambda-DRT). L'avantage majeur d'une telle sémantique profonde est de repré- senter le sens par des formules logiques aisément exploitables, par exemple par un moteur d'inférence. Déterminants et quantificateurs jouent un rôle fondamental dans la construction de ces formules. Mais dans notre système de types complexes, leurs termes sémantiques usuels ne fonctionnent pas. Nous proposons une solution inspirée des opérateurs epsilon et tau de Hilbert, sorte de fonctions de choix et d'éléments génériques. Cela unifie le traitement des différents types de déterminants et de quantificateurs ainsi que le liage dynamique des pronoms. Surtout, cette modélisation totalement calculable s'intègre parfaitement dans l'analyseur à large échelle du français Grail, tant en théorie qu'en pratique

Zero and Few-shot Semantic Parsing with Ambiguous Inputs

Despite the frequent challenges posed by ambiguity when representing meaning via natural language, it is often ignored or deliberately removed in tasks mapping language to formally-designed representations, which generally assume a one-to-one mapping between linguistic and formal representations. We attempt to address this shortcoming by introducing AmP, a framework, dataset, and challenge for translating ambiguous natural language to formal representations like logic and code. We define templates and generate data for five well-documented linguistic ambiguities. Using AmP, we investigate how several few-shot text-to-code systems handle ambiguity, introducing three new metrics. We find that large pre-trained models perform poorly at capturing the distribution of possible meanings without deliberate instruction. However, models are able to capture the distribution well when ambiguity is attested in their inputs. These results motivate a call for including ambiguity explicitly in datasets and promote considering the distribution of possible outputs when evaluating systems. Data and code: https://github.com/esteng/ambiguous_parsingComment: ICLR 2024 Camera Read