A Deep-Parsing Approach to Natural Language Understanding in Dialogue System: Results of a Corpus-Based Evaluation

Alexandre A. J. Denis (1) , Matthieu Quignard (1) , Guillaume Pitel (1)

1 TALARIS - Natural Language Processing: representation, inference and semantics

This paper presents an approach to dialogue understanding based on a deep parsing and rule-based semantic analysis. Its performance in the semantic evaluation performed in the framework of the EVALDA/MEDIA campaign is encouraging. The MEDIA project aims to evaluate natural language understanding systems for French on a hotel reservation task (Devillers et al., 2004). For the evaluation, five participating teams had to produce an annotated version of the input utterances in compliance with a commonly agreed format (the MEDIA formalism). An approach based on symbolic processing was not straightforward given the conditions of the evaluation but we achieved a score close to that of statistical systems, without needing an annotated corpus. Despite the architecture has been designed for this campaign, exclusively dedicated to spoken dialogue understanding, we believe that our approach based on a LTAG parser and two ontologies can be used in real dialogue systems, providing quite robust speech understanding and facilities for interfacing with a dialogue manager and the application itself.

Mots clés: Deep-parsing; Syntax; Semantic; Ontology; Natural Language Understanding

Domaines: Interface homme-machine [cs.HC]

Alexandre A. J. Denis, Matthieu Quignard, Guillaume Pitel. A Deep-Parsing Approach to Natural Language Understanding in Dialogue System: Results of a Corpus-Based Evaluation. Proceedings of Language Resources and Evaluation Conference, 2006, Genoa, Italy. pp.339-344. ⟨inria-00112897⟩