Using Paraphrases and Lexical Semantics to Improve the Accuracy and the Robustness of Supervised Models in Situated Dialogue Systems

International audienceThis paper explores to what extent lemmatisation, lexical resources, distributional semantics and paraphrases can increase the accuracy of supervised models for dialogue management. The results suggest that each of these factors can help improve performance but that the impact will vary depending on their combination and on the evaluation mode

What should I do now? Supporting conversations in a serious game.

International audienceWe present two dialogue systems developed to support chating with French speaking virtual characters in the context of a serious game: one hybrid statistical/symbolic and one purely statistical. The player is guided in the quest by different interactions including twelve distinct dialogs with different virtual characters

Weakly supervised discriminative training of linear models for Natural Language Processing

International audienceThis work explores weakly supervised training of discrimi-native linear classifiers. Such features-rich classifiers have been widely adopted by the Natural Language processing (NLP) community because of their powerful modeling capacity and their support for correlated features , which allow separating the expert task of designing features from the core learning method. However, unsupervised training of discrim-inative models is more challenging than with generative models. We adapt a recently proposed approximation of the classifier risk and derive a closed-form solution that greatly speeds-up its convergence time. This method is appealing because it provably converges towards the minimum risk without any labeled corpus, thanks to only two reasonable assumptions about the rank of class marginal and Gaussianity of class-conditional linear scores. We also show that the method is a viable , interesting alternative to achieve weakly supervised training of linear classifiers in two NLP tasks: predicate and entity recognition

Enhanced discriminative models with tree kernels and unsupervised training for entity detection

International audienceThis work explores two approaches to improve the discriminative models that are commonly used nowadays for entity detection: tree-kernels and unsupervised training. Feature-rich classifiers have been widely adopted by the Natural Language processing (NLP) community because of their powerful modeling capacity and their support for correlated features, which allow separating the expert task of designing features from the core learning method. The first proposed approach consists in leveraging the fast and efficient linear models with unsupervised training, thanks to a recently proposed approximation of the classifier risk, an appealing method that provably converges towards the minimum risk without any labeled corpus. In the second proposed approach, tree kernels are used with support vector machines to exploit dependency structures for entity detection , which relieve designers from the burden of carefully design rich syntactic features manually. We study both approaches on the same task and corpus and show that they offer interesting alternatives to supervised learning for entity recognition

Adaptable dialogue architecture and runtime engine (AdaRTE): A framework for rapid prototyping of health dialog systems

International audienceSpoken dialog systems have been increasingly employed to provide ubiquitous access via telephone to information and services for the non-Internet-connected public. They have been successfully applied in the health care context; however, speech technology requires a considerable development investment. The advent of VoiceXML reduced the proliferation of incompatible dialog formalisms, at the expense of adding even more complexity. This paper introduces a novel architecture for dialogue representation and interpretation, AdaRTE, which allows developers to lay out dialog interactions through a high-level formalism, offering both declarative and procedural features. AdaRTE's aim is to provide a ground for deploying complex and adaptable dialogs whilst allowing experimentation and incremental adoption of innovative speech technologies. It enhances augmented transition networks with dynamic behavior, and drives multiple back-end realizers, including VoiceXML. It has been especially targeted to the health care context, because of the great scale and the need for reducing the barrier to a widespread adoption of dialog systems

Bayesian Inverse Reinforcement Learning for Modeling Conversational Agents in a Virtual Environment.

International audienceThis work proposes a Bayesian approach to learn the behavior of hu- man characters that give advice and help users to complete tasks in a situated environment. We apply Bayesian Inverse Reinforcement Learning (BIRL) to in- fer this behavior in the context of a serious game, given evidence in the form of stored dialogues provided by experts who play the role of several conversational agents in the game. We show that the proposed approach converges relatively quickly and that it outperforms two baseline systems, including a dialogue man- ager trained to provide "locally" optimal decisions

Building and Exploiting a Corpus of Dialog Interactions between French Speaking Virtual and Human Agents

International audienceWe describe the acquisition of a dialog corpus for French based on multi-task human-machine interactions in a serious game setting. We present a tool for data collection that is configurable for multiple games; describe the data collected using this tool and the annotation schema used to annotate it; and report on the results obtained when training a classifier on the annotated data to associate each player turn with a dialog move usable by a rule based dialog manager. The collected data consists of approximately 1250 dialogs, 10454 utterances and 168509 words and will be made freely available to academic and nonprofit research

An End-to-End Evaluation of Two Situated Dialog Systems.

International audienceWe present and evaluate two state-of-the art dialogue systems developed to support dialog with French speaking virtual characters in the context of a serious game: one hybrid statistical/symbolic and one purely statistical. We conducted a quantitative evaluation where we compare the accuracy of the interpreter and of the dialog manager used by each system; a user based evaluation based on 22 subjects using both the statistical and the hybrid system; and a corpus based evaluation where we examine such criteria as dialog coherence, dialog success, interpretation and generation errors in the corpus of Human-System interactions collected during the user-based evaluation. We show that although the statistical approach is slightly more robust, the hybrid strategy seems to be better at guiding the player through the game

Are cascade dialogue state tracking models speaking out of turn in spoken dialogues?

In Task-Oriented Dialogue (TOD) systems, correctly updating the system's understanding of the user's needs is key to a smooth interaction. Traditionally TOD systems are composed of several modules that interact with one another. While each of these components is the focus of active research communities, their behavior in interaction can be overlooked. This paper proposes a comprehensive analysis of the errors of state of the art systems in complex settings such as Dialogue State Tracking which highly depends on the dialogue context. Based on spoken MultiWoz, we identify that errors on non-categorical slots' values are essential to address in order to bridge the gap between spoken and chat-based dialogue systems. We explore potential solutions to improve transcriptions and help dialogue state tracking generative models correct such errors.Comment: Submitted to IEEE ICASSP 2024{\copyright} 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work

Associating Automatic Natural Language Processing to Serious Games and Virtual Worlds

International audienceWe present our current research activities associating automatic natural language processing to serious games and virtual worlds. Several interesting scenarios have been developed: language learning, natural language generation, multilingual information, emotion detection, real-time translations, and non-intrusive access to linguistic information such as definitions or synonyms. Part of our work has contributed to the specification of the Multi Lingual Information Framework [ISO FDIS 24616], (MLIF,2011). Standardization will grant stability, interoperability and sustainability of an important part of our research activities, in particular, in the framework of representing and managing multilingual textual information