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

International audienceThis 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

Survey on Evaluation Methods for Dialogue Systems

In this paper we survey the methods and concepts developed for the evaluation of dialogue systems. Evaluation is a crucial part during the development process. Often, dialogue systems are evaluated by means of human evaluations and questionnaires. However, this tends to be very cost and time intensive. Thus, much work has been put into finding methods, which allow to reduce the involvement of human labour. In this survey, we present the main concepts and methods. For this, we differentiate between the various classes of dialogue systems (task-oriented dialogue systems, conversational dialogue systems, and question-answering dialogue systems). We cover each class by introducing the main technologies developed for the dialogue systems and then by presenting the evaluation methods regarding this class

The BURCHAK corpus: a Challenge Data Set for Interactive Learning of Visually Grounded Word Meanings

We motivate and describe a new freely available human-human dialogue dataset for interactive learning of visually grounded word meanings through ostensive definition by a tutor to a learner. The data has been collected using a novel, character-by-character variant of the DiET chat tool (Healey et al., 2003; Mills and Healey, submitted) with a novel task, where a Learner needs to learn invented visual attribute words (such as " burchak " for square) from a tutor. As such, the text-based interactions closely resemble face-to-face conversation and thus contain many of the linguistic phenomena encountered in natural, spontaneous dialogue. These include self-and other-correction, mid-sentence continuations, interruptions, overlaps, fillers, and hedges. We also present a generic n-gram framework for building user (i.e. tutor) simulations from this type of incremental data, which is freely available to researchers. We show that the simulations produce outputs that are similar to the original data (e.g. 78% turn match similarity). Finally, we train and evaluate a Reinforcement Learning dialogue control agent for learning visually grounded word meanings, trained from the BURCHAK corpus. The learned policy shows comparable performance to a rule-based system built previously.Comment: 10 pages, THE 6TH WORKSHOP ON VISION AND LANGUAGE (VL'17