Modèle de comportement communicatif conventionnel pour un agent en interaction avec des humains (Approche par jeux de dialogue)

Cette thèse a pour objectif l amélioration des capacités communicatives des agents logiciels en interaction avec des humains. Dans ce but, nous proposons une méthodologie basée sur l étude d un corpus d interactions Homme-Homme orientées vers la réalisation d une tâche. Nous proposons un cadre qui s appuie sur les jeux de dialogue afin de modéliser des motifs dialogiques observés. Nous illustrons la spécification de tels jeux depuis des motifs extraits en appliquant l'ensemble des étapes de noter méthodologie à un corpus. Les jeux spécifiés sont validés en montrant qu ils décrivent de façon appropriée les motifs apparaissant dans le corpus de référence. Enfin, nous montrons l intérêt interprétatif et génératif de notre modèle pour le fondement du comportement communicatif conventionnel d un agent interagissant avec un humain. Nous implémentons ce modèle dans le module Dogma, exploitable par un agent dans un dialogue impliquant deux interlocuteurs.This research work aims at improving the communicative behaviour of software agents interacting with humans. To this purpose, we present a data-driven methodology based on the study of a task oriented corpus consisting of Human-Human interactions. We present a framework to specify dialogue games from observed interaction patterns based on the notion of social commitments and conversational gameboard. We exemplify the specification of dialogue games by implementing all the steps of our methodology ona task-oriented corpus. The produced games are validated by showing that they appropriately describe the patterns appearing in a reference corpus. Eventually, we show that an agent can take advantage of our model to regulate its conventional communicative behaviour on both interpretative and generative levels. We implement this model into Dogma, a module that can be used by an agent to manage its communicative behaviour in a two-interlocutor dialogue.ROUEN-INSA Madrillet (765752301) / SudocSudocFranceF

"How May I Help You?": Modeling Twitter Customer Service Conversations Using Fine-Grained Dialogue Acts

Given the increasing popularity of customer service dialogue on Twitter, analysis of conversation data is essential to understand trends in customer and agent behavior for the purpose of automating customer service interactions. In this work, we develop a novel taxonomy of fine-grained "dialogue acts" frequently observed in customer service, showcasing acts that are more suited to the domain than the more generic existing taxonomies. Using a sequential SVM-HMM model, we model conversation flow, predicting the dialogue act of a given turn in real-time. We characterize differences between customer and agent behavior in Twitter customer service conversations, and investigate the effect of testing our system on different customer service industries. Finally, we use a data-driven approach to predict important conversation outcomes: customer satisfaction, customer frustration, and overall problem resolution. We show that the type and location of certain dialogue acts in a conversation have a significant effect on the probability of desirable and undesirable outcomes, and present actionable rules based on our findings. The patterns and rules we derive can be used as guidelines for outcome-driven automated customer service platforms.Comment: 13 pages, 6 figures, IUI 201

Simultaneous Machine Translation with Large Language Models

Large language models (LLM) have demonstrated their abilities to solve various natural language processing tasks through dialogue-based interactions. For instance, research indicates that LLMs can achieve competitive performance in offline machine translation tasks for high-resource languages. However, applying LLMs to simultaneous machine translation (SimulMT) poses many challenges, including issues related to the training-inference mismatch arising from different decoding patterns. In this paper, we explore the feasibility of utilizing LLMs for SimulMT. Building upon conventional approaches, we introduce a simple yet effective mixture policy that enables LLMs to engage in SimulMT without requiring additional training. Furthermore, after Supervised Fine-Tuning (SFT) on a mixture of full and prefix sentences, the model exhibits significant performance improvements. Our experiments, conducted with Llama2-7B-chat on nine language pairs from the MUST-C dataset, demonstrate that LLM can achieve translation quality and latency comparable to dedicated SimulMT models

Dialogue Act Modeling for Automatic Tagging and Recognition of Conversational Speech

We describe a statistical approach for modeling dialogue acts in conversational speech, i.e., speech-act-like units such as Statement, Question, Backchannel, Agreement, Disagreement, and Apology. Our model detects and predicts dialogue acts based on lexical, collocational, and prosodic cues, as well as on the discourse coherence of the dialogue act sequence. The dialogue model is based on treating the discourse structure of a conversation as a hidden Markov model and the individual dialogue acts as observations emanating from the model states. Constraints on the likely sequence of dialogue acts are modeled via a dialogue act n-gram. The statistical dialogue grammar is combined with word n-grams, decision trees, and neural networks modeling the idiosyncratic lexical and prosodic manifestations of each dialogue act. We develop a probabilistic integration of speech recognition with dialogue modeling, to improve both speech recognition and dialogue act classification accuracy. Models are trained and evaluated using a large hand-labeled database of 1,155 conversations from the Switchboard corpus of spontaneous human-to-human telephone speech. We achieved good dialogue act labeling accuracy (65% based on errorful, automatically recognized words and prosody, and 71% based on word transcripts, compared to a chance baseline accuracy of 35% and human accuracy of 84%) and a small reduction in word recognition error.Comment: 35 pages, 5 figures. Changes in copy editing (note title spelling changed

Domain transfer for deep natural language generation from abstract meaning representations

Stochastic natural language generation systems that are trained from labelled datasets are often domainspecific in their annotation and in their mapping from semantic input representations to lexical-syntactic outputs. As a result, learnt models fail to generalize across domains, heavily restricting their usability beyond single applications. In this article, we focus on the problem of domain adaptation for natural language generation. We show how linguistic knowledge from a source domain, for which labelled data is available, can be adapted to a target domain by reusing training data across domains. As a key to this, we propose to employ abstract meaning representations as a common semantic representation across domains. We model natural language generation as a long short-term memory recurrent neural network encoderdecoder, in which one recurrent neural network learns a latent representation of a semantic input, and a second recurrent neural network learns to decode it to a sequence of words. We show that the learnt representations can be transferred across domains and can be leveraged effectively to improve training on new unseen domains. Experiments in three different domains and with six datasets demonstrate that the lexical-syntactic constructions learnt in one domain can be transferred to new domains and achieve up to 75-100% of the performance of in-domain training. This is based on objective metrics such as BLEU and semantic error rate and a subjective human rating study. Training a policy from prior knowledge from a different domain is consistently better than pure in-domain training by up to 10%

A Robust and Efficient Three-Layered Dialogue Component for a Speech-to-Speech Translation System

We present the dialogue component of the speech-to-speech translation system VERBMOBIL. In contrast to conventional dialogue systems it mediates the dialogue while processing maximally 50% of the dialogue in depth. Special requirements like robustness and efficiency lead to a 3-layered hybrid architecture for the dialogue module, using statistics, an automaton and a planner. A dialogue memory is constructed incrementally.Comment: Postscript file, compressed and uuencoded, 15 pages, to appear in Proceedings of EACL-95, Dublin

Transactional distance in a blended learning environment

This paper presents a case study that describes and discusses the problems encountered during the design and implementation of a blended learning course, largely taught online through a web-based learning environment. Based on Moore's theory of transactional distance, the course was explicitly designed to have dialogue at its heart. However, the reality of systemic behaviours caused by delivering such a course within a group of conventional further and higher educational institutions has led to an entirely unanticipated reversion to structure, with unpleasant consequences for both quality and quantity of dialogue. The paper looks at some of the reasons for this drift, and suggests that some of the disappointing results (in particular in terms of the quality of the students' experience and associated poor retention) can be attributed to the lack of dialogue, and consequent increase in transactional distance. It concludes with a description and evaluation of steps currently being taken to correct this behaviour