Word Order Phenomena in Spoken French : a Study on Four Corpora of Task-Oriented Dialogue and its Consequences on Language Processing

International audienceThis paper presents a corpus study that investigates the question of word order variations (WOV) in spontaneous spoken French and its consequences on the parsing techniques that are used in Natural Language Processing. We have studied four taskoriented spoken dialogue corpora which concern different application tasks (air transport or tourism information, switchboard calls). Two corpora concern phone conversations while the other two correspond to direct interaction. Every word order variation has been manually annotated by 3 experts, following a cross-validation procedure. Our results show that, while conversational spoken French should be highly affected by WOVs, it should also still be considered as a rigid order language: WOVs follow some impressive structural regularity and they result very rarely in discontinuous syntactic structures. As a result, non-projective parsers remain well adapted to conversational spoken French

LeBenchmark 2.0: a Standardized, Replicable and Enhanced Framework for Self-supervised Representations of French Speech

Self-supervised learning (SSL) is at the origin of unprecedented improvements in many different domains including computer vision and natural language processing. Speech processing drastically benefitted from SSL as most of the current domain-related tasks are now being approached with pre-trained models. This work introduces LeBenchmark 2.0 an open-source framework for assessing and building SSL-equipped French speech technologies. It includes documented, large-scale and heterogeneous corpora with up to 14,000 hours of heterogeneous speech, ten pre-trained SSL wav2vec 2.0 models containing from 26 million to one billion learnable parameters shared with the community, and an evaluation protocol made of six downstream tasks to complement existing benchmarks. LeBenchmark 2.0 also presents unique perspectives on pre-trained SSL models for speech with the investigation of frozen versus fine-tuned downstream models, task-agnostic versus task-specific pre-trained models as well as a discussion on the carbon footprint of large-scale model training.Comment: Under submission at Computer Science and Language. Preprint allowe

How NLP techniques can improve speech understanding: ROMUS - a Robust Chunk based Message Understanding System Using Link Grammars

This paper discusses the issue of how a speech understanding system can be made robust against spontaneous speech phenomena (hesitations and repairs) as well as achieving a detailed analysis of spoken French. The Romus system is presented. It implements speech understanding in a two-stage process. The first stage achieves a finite-state shallow parsing that consists in segmenting the recognized sentence into basic units (spokenadapted chunks). The second one, a Link Grammar parser, looks for inter-chunks dependencies in order to build a rich representation of the semantic structure of the utterance. These dependencies are mainly investigated at a pragmatic level through the consideration of a task concept hierarchy. Discussion about the approach adopted, its benefits and limitations, is based on the results of the system's assessment carried out under different linguistic phenomena during an evaluation campaign held by the French CNRS

Comparisons of Relatedness Measures through a Word Sense Disambiguation Task

International audienceMichael Zock's work has focussed these last years on finding the appro- priate and most adequate word when writing or speaking. The semantic relatedness between words can play an important role in this context. We present here a novel approach to analyse the semantic relatedness between words based on the relevance of semantic relatedness measures on the global level of a word sense disambigua- tion task. We introduce two metrics : the best atteignable score and the correlation between global score and the F1 measure. We use them to analyse several classical local semantic similarity measures as well as measures built by our team

Obtaining predictive results with an objective evaluation of spoken dialogue systems : experiments with the DCR assessment paradigm

The DCR methodology is a framework that proposes a generic and detailed evaluation of spoken dialog systems. We have already detailed (Antoine et al., 1998) the theoretical bases of this paradigm. In this paper, we present some experimental results on spoken language understanding that show the feasibility and the reliability of the DCR evaluation as well as its ability to provide a detailed diagnosis of the system's behaviour. Finally, we highlight the extension of the DCR methodology to dialogue management. 1. Introduction During the last decade, the development of spoken language technologies has gone along with the achievement of large evaluation programs which concern spoken dialogue systems as well as some of their components (speech recognition, spoken language understanding, dialogue management). Generally speaking, this evaluation is based on the computation of quantitative metrics that intend to offer an objective and reproducible survey of the system's behaviour. For insta..

Stratégie d'analyse détaillée pour la compréhension automatique robuste de la parole

This PHD focusses on speech understanding in man-machine communication. We discuss the issue of how a speech understanding system can be made robust against spontaneous speech phenomena as well as achieving a detailed analysis of spoken French. We argue that a detailed linguistic analysis (with both syntax and semantics) is essential for correctly process spoken utterances and is also a necesary condition to develop applications that are not entirely dedicated to a very specific task but present sufficient genericity. The system presented (ROMUS) implements speech understanding in a two-satge process. The first one achieves a finite-state shallow parsing consists in segmenting the utterance into basic units (spoken adaptated chunks). This stage is generic and is motivated by the regularities observed in spoken French. The second one, a Link Grammar parser, looks for inter-chunks dependencies in order to build a rich representation of the semantic structure of the utterance.Nous présentons une stratégie robuste d analyse des énonces oraux pour la compréhension hors-contexte de la parole en dialogue homme-machine finalisé. Nous faisons l'hypothese qu'une analyse détaillée des énoncés oraux (associant syntaxe et sémantique est essentielle au traitement correct des énonces et est la condition nécessaire au developpement d'applications non plus limitées a des cadres très finalisés mais faisant preuve d'une certaine généricité. Dans le système proposé ROMUS applique au renseignement touristique, une analyse syntaxique partielle de surface (cascades de transducteurs) permet tout dabord la segmentation de l'énonce en groupes minimaux élémentaires. Cette etape, générique, est motivée par la prise en compte explicite et intrinsèque des régularités observées dans les productions orales. Une analyse globale des dépendances sémantico-pragamatiques entre les segments (grammaires des liens) permet ensuite de déduire la représentation sémantique de l'énoncé.LORIENT-BU (561212106) / SudocSudocFranceF