Surface realisation (SR) consists in generating a text from a meaning representations (MR). In this paper, we introduce a new parallel dataset of deep meaning representations (MR) and French sentences and we present a novel method for MR-to-text generation which seeks to generalise by abstracting away from lexical content. Most current work on natural language generation focuses on generating text that matches a reference using BLEU as evaluation criteria. In this paper, we additionally consider the model’s ability to reintroduce the function words that are absent from the deep input meaning representations. We show that our approach increases both BLEU score and the scores used to assess function words generation.

We study the automatic generation of syntactic paraphrases using four different models for generation: data-to-text generation, text-to-text generation, text reduction and text expansion, We derive training data for each of these tasks from the WebNLG dataset and we show (i) that conditioning generation on syntactic constraints effectively permits the generation of syntactically distinct paraphrases for the same input and (ii) that exploiting different types of input (data, text or data+text) further increases the number of distinct paraphrases that can be generated for a given input.

Nous proposons une nouvelle méthode pour la création automatique de grammaires lexicalisées syntaxico-sémantiques. A l’heure actuelle, la création de grammaire résulte soit d’un travail manuel soit d’un traitement automatisé de corpus arboré. Notre proposition est d’extraire à partir de données VerbNet une grammaire noyau (formes canoniques des verbes et des groupes nominaux) de l’anglais intégrant une sémantique VerbNet. Notre objectif est de profiter des larges ressources existantes pour produire un système de génération de texte symbolique de qualité en domaine restreint.