Richard Moot


2023

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SPINDLE: Spinning Raw Text into Lambda Terms with Graph Attention
Konstantinos Kogkalidis | Michael Moortgat | Richard Moot
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations

This paper describes SPINDLE, an open source Python module, providing an efficient and accurate parser for written Dutch that transforms raw text input to programs for meaning composition expressed as λ terms. The parser integrates a number of breakthrough advances made in recent years. Its output consists of hi-res derivations of a multimodal type-logical grammar, capturing two orthogonal axes of syntax, namely deep function-argument structures and dependency relations. These are produced by three interdependent systems: a static type-checker asserting the well-formedness of grammatical analyses, a state-of-the-art, structurally-aware supertagger based on heterogeneous graph convolutions, and a massively parallel proof search component based on Sinkhorn iterations. Packed in the software are also handy utilities and extras for proof visualization and inference, intended to facilitate end-user utilization.

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DACCORD : un jeu de données pour la Détection Automatique d’énonCés COntRaDictoires en français
Maximos Skandalis | Richard Moot | Simon Robillard
Actes de CORIA-TALN 2023. Actes de la 30e Conférence sur le Traitement Automatique des Langues Naturelles (TALN), volume 1 : travaux de recherche originaux -- articles longs

La tâche de détection automatique de contradictions logiques entre énoncés en TALN est une tâche de classification binaire, où chaque paire de phrases reçoit une étiquette selon que les deux phrases se contredisent ou non. Elle peut être utilisée afin de lutter contre la désinformation. Dans cet article, nous présentons DACCORD, un jeu de données dédié à la tâche de détection automatique de contradictions entre phrases en français. Le jeu de données élaboré est actuellement composé de 1034 paires de phrases. Il couvre les thématiques de l’invasion de la Russie en Ukraine en 2022, de la pandémie de Covid-19 et de la crise climatique. Pour mettre en avant les possibilités de notre jeu de données, nous évaluons les performances de certains modèles de transformeurs sur lui. Nous constatons qu’il constitue pour eux un défi plus élevé que les jeux de données existants pour le français, qui sont déjà peu nombreux. In NLP, the automatic detection of logical contradictions between statements is a binary classification task, in which a pair of sentences receives a label according to whether or not the two sentences contradict each other. This task has many potential applications, including combating disinformation. In this article, we present DACCORD, a new dataset dedicated to the task of automatically detecting contradictions between sentences in French. The dataset is currently composed of 1034 sentence pairs. It covers the themes of Russia’s invasion of Ukraine in 2022, the Covid-19 pandemic, and the climate crisis. To highlight the possibilities of our dataset, we evaluate the performance of some recent Transformer models on it. We conclude that our dataset is considerably more challenging than the few existing datasets for French.

2020

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Neural Proof Nets
Konstantinos Kogkalidis | Michael Moortgat | Richard Moot
Proceedings of the 24th Conference on Computational Natural Language Learning

Linear logic and the linear λ-calculus have a long standing tradition in the study of natural language form and meaning. Among the proof calculi of linear logic, proof nets are of particular interest, offering an attractive geometric representation of derivations that is unburdened by the bureaucratic complications of conventional prooftheoretic formats. Building on recent advances in set-theoretic learning, we propose a neural variant of proof nets based on Sinkhorn networks, which allows us to translate parsing as the problem of extracting syntactic primitives and permuting them into alignment. Our methodology induces a batch-efficient, end-to-end differentiable architecture that actualizes a formally grounded yet highly efficient neuro-symbolic parser. We test our approach on ÆThel, a dataset of type-logical derivations for written Dutch, where it manages to correctly transcribe raw text sentences into proofs and terms of the linear λ-calculus with an accuracy of as high as 70%.

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ÆTHEL: Automatically Extracted Typelogical Derivations for Dutch
Konstantinos Kogkalidis | Michael Moortgat | Richard Moot
Proceedings of the Twelfth Language Resources and Evaluation Conference

We present ÆTHEL, a semantic compositionality dataset for written Dutch. ÆTHEL consists of two parts. First, it contains a lexicon of supertags for about 900 000 words in context. The supertags correspond to types of the simply typed linear lambda-calculus, enhanced with dependency decorations that capture grammatical roles supplementary to function-argument structures. On the basis of these types, ÆTHEL further provides 72 192 validated derivations, presented in four formats: natural-deduction and sequent-style proofs, linear logic proofnets and the associated programs (lambda terms) for meaning composition. ÆTHEL’s types and derivations are obtained by means of an extraction algorithm applied to the syntactic analyses of LASSY Small, the gold standard corpus of written Dutch. We discuss the extraction algorithm and show how ‘virtual elements’ in the original LASSY annotation of unbounded dependencies and coordination phenomena give rise to higher-order types. We suggest some example usecases highlighting the benefits of a type-driven approach at the syntax semantics interface. The following resources are open-sourced with ÆTHEL: the lexical mappings between words and types, a subset of the dataset consisting of 7 924 semantic parses, and the Python code that implements the extraction algorithm.

2012

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Sémantique à large couverture et raisonnement spatio-temporelle [Wide-coverage semantics for spatio-temporal reasoning]
Richard Moot
Traitement Automatique des Langues, Volume 53, Numéro 2 : Traitement automatique des informations temporelles et spatiales en langage naturel [Automatic Processing for Temporal and Spatial Information in Natural Language]

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Traitement automatique sur corpus de récits de voyages pyrénéens : Une analyse syntaxique, sémantique et temporelle (Processing of a Pyrenees Travel Novels Corpus : a Syntactical, Semantical and Temporal Analysis.) [in French]
Anaïs Lefeuvre | Richard Moot | Christian Retoré | Noémie-Fleur Sandillon-Rezer
Proceedings of the Joint Conference JEP-TALN-RECITAL 2012, volume 2: TALN

2011

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Un calcul de termes typés pour la pragmatique lexicale: chemins et voyageurs fictifs dans un corpus de récits de voyage (A calculation of typed terms for lexical pragmatics: paths and fictional travellers in a travel stories corpus)
Richard Moot | Laurent Prévot | Christian Retoré
Actes de la 18e conférence sur le Traitement Automatique des Langues Naturelles. Articles courts

Ce travail s’inscrit dans l’analyse automatique d’un corpus de récits de voyage. À cette fin, nous raffinons la sémantique de Montague pour rendre compte des phénomènes d’adaptation du sens des mots au contexte dans lequel ils apparaissent. Ici, nous modélisons les constructions de type ‘le chemin descend pendant une demi-heure’ où ledit chemin introduit un voyageur fictif qui le parcourt, en étendant des idées que le dernier auteur a développé avec Bassac et Mery. Cette introduction du voyageur utilise la montée de type afin que le quantificateur introduisant le voyageur porte sur toute la phrase et que les propriétés du chemin ne deviennent pas des propriétés du voyageur, fût-il fictif. Cette analyse sémantique (ou plutôt sa traduction en lambda-DRT) est d’ores et déjà implantée pour une partie du lexique de Grail.

2010

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Semi-automated Extraction of a Wide-Coverage Type-Logical Grammar for French
Richard Moot
Actes de la 17e conférence sur le Traitement Automatique des Langues Naturelles. Articles courts

The paper describes the development of a wide-coverage type-logical grammar for French, which has been extracted from the Paris 7 treebank and received a significant amount of manual verification and cleanup. The resulting treebank is evaluated using a supertagger and performs at a level comparable to the best supertagging results for English.

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Wide-Coverage French Syntax and Semantics using Grail
Richard Moot
Actes de la 17e conférence sur le Traitement Automatique des Langues Naturelles. Démonstrations

The system demo introduces Grail, a general-purpose parser for multimodal categorial grammars, with special emphasis on recent research which makes Grail suitable for wide-coverage French syntax and semantics. These developments have been possible thanks to a categorial grammar which has been extracted semi-automatically from the Paris 7 treebank and a semantic lexicon which maps word, part-of-speech tags and formulas combinations to Discourse Representation Structures.

2008

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Lambek Grammars, Tree Adjoining Grammars and Hyperedge Replacement Grammars
Richard Moot
Proceedings of the Ninth International Workshop on Tree Adjoining Grammar and Related Frameworks (TAG+9)

2002

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Using the Spoken Dutch Corpus for type-logical grammar induction
Michael Moortgat | Richard Moot
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)