170 citations · 449 across the 35 of their papers we have counts for
30 papers · 1 filter
Neural Models and Language Model Prompting for the Multidimensional Evaluation of Open-Ended Conversations
Michelle Elizabeth, Alicja Kasicka, Natalia Krawczyk +4
The growing number of generative AI-based dialogue systems has made their evaluation a crucial challenge. This paper presents our contribution to this important problem through the…
Exploring ReAct Prompting for Task-Oriented Dialogue: Insights and Shortcomings
Michelle Elizabeth, Morgan Veyret, Miguel Couceiro +2
Large language models (LLMs) gained immense popularity due to their impressive capabilities in unstructured conversations. Empowering LLMs with advanced prompting strategies such a…
Talking to Machines: do you read me?
Lina M. Rojas-Barahona
In this dissertation I would like to guide the reader to the research on dialogue but more precisely the research I have conducted during my career since my PhD thesis. Starting fr…
Question Generation in Knowledge-Driven Dialog: Explainability and Evaluation
Juliette Faille, Quentin Brabant, Gwenole Lecorve +2
We explore question generation in the context of knowledge-grounded dialogs focusing on explainability and evaluation. Inspired by previous work on planning-based summarisation, we…
WEBDial, a Multi-domain, Multitask Statistical Dialogue Framework with RDF
Morgan Veyret, Jean-Baptiste Duchene, Kekeli Afonouvi +3
Typically available dialogue frameworks have adopted a semantic representation based on dialogue-acts and slot-value pairs. Despite its simplicity, this representation has disadvan…
Unsupervised Auditory and Semantic Entrainment Models with Deep Neural Networks
Jay Kejriwal, Stefan Benus, Lina M. Rojas-Barahona
Speakers tend to engage in adaptive behavior, known as entrainment, when they become similar to their interlocutor in various aspects of speaking. We present an unsupervised deep l…
Are cascade dialogue state tracking models speaking out of turn in spoken dialogues?
Lucas Druart, Léo Jacqmin, Benoît Favre +2
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…
Few-Shot Structured Policy Learning for Multi-Domain and Multi-Task Dialogues
Thibault Cordier, Tanguy Urvoy, Fabrice Lefevre +1
Reinforcement learning has been widely adopted to model dialogue managers in task-oriented dialogues. However, the user simulator provided by state-of-the-art dialogue frameworks a…
Investigating the Effect of Relative Positional Embeddings on AMR-to-Text Generation with Structural Adapters
Sebastien Montella, Alexis Nasr, Johannes Heinecke +2
Text generation from Abstract Meaning Representation (AMR) has substantially benefited from the popularized Pretrained Language Models (PLMs). Myriad approaches have linearized the…
Graph Neural Network Policies and Imitation Learning for Multi-Domain Task-Oriented Dialogues
Thibault Cordier, Tanguy Urvoy, Fabrice Lefèvre +1
Task-oriented dialogue systems are designed to achieve specific goals while conversing with humans. In practice, they may have to handle simultaneously several domains and tasks. T…
"Do you follow me?": A Survey of Recent Approaches in Dialogue State Tracking
Léo Jacqmin, Lina M. Rojas-Barahona, Benoit Favre
While communicating with a user, a task-oriented dialogue system has to track the user's needs at each turn according to the conversation history. This process called dialogue stat…
CoQAR: Question Rewriting on CoQA
Quentin Brabant, Gwenole Lecorve, Lina M. Rojas-Barahona
Questions asked by humans during a conversation often contain contextual dependencies, i.e., explicit or implicit references to previous dialogue turns. These dependencies take the…
Active Learning and Multi-label Classification for Ellipsis and Coreference Detection in Conversational Question-Answering
Quentin Brabant, Lina Maria Rojas-Barahona, Claire Gardent
In human conversations, ellipsis and coreference are commonly occurring linguistic phenomena. Although these phenomena are a mean of making human-machine conversations more fluent…
Hyperbolic Temporal Knowledge Graph Embeddings with Relational and Time Curvatures
Sebastien Montella, Lina Rojas-Barahona, Johannes Heinecke
Knowledge Graph (KG) completion has been excessively studied with a massive number of models proposed for the Link Prediction (LP) task. The main limitation of such models is their…
Is the User Enjoying the Conversation? A Case Study on the Impact on the Reward Function
Lina M. Rojas-Barahona
The impact of user satisfaction in policy learning task-oriented dialogue systems has long been a subject of research interest. Most current models for estimating the user satisfac…
Denoising Pre-Training and Data Augmentation Strategies for Enhanced RDF Verbalization with Transformers
Sebastien Montella, Betty Fabre, Tanguy Urvoy +2
The task of verbalization of RDF triples has known a growth in popularity due to the rising ubiquity of Knowledge Bases (KBs). The formalism of RDF triples is a simple and efficien…
Diluted Near-Optimal Expert Demonstrations for Guiding Dialogue Stochastic Policy Optimisation
Thibault Cordier, Tanguy Urvoy, Lina M. Rojas-Barahona +1
A learning dialogue agent can infer its behaviour from interactions with the users. These interactions can be taken from either human-to-human or human-machine conversations. Howev…
Addressing Objects and Their Relations: The Conversational Entity Dialogue Model
Stefan Ultes, Paweł Budzianowski, Iñigo Casanueva +5
Statistical spoken dialogue systems usually rely on a single- or multi-domain dialogue model that is restricted in its capabilities of modelling complex dialogue structures, e.g.,…
Deep learning for language understanding of mental health concepts derived from Cognitive Behavioural Therapy
Lina Rojas-Barahona, Bo-Hsiang Tseng, Yinpei Dai +5
In recent years, we have seen deep learning and distributed representations of words and sentences make impact on a number of natural language processing tasks, such as similarity,…
Nearly Zero-Shot Learning for Semantic Decoding in Spoken Dialogue Systems
Lina M. Rojas-Barahona, Stefan Ultes, Pawel Budzianowski +4
This paper presents two ways of dealing with scarce data in semantic decoding using N-Best speech recognition hypotheses. First, we learn features by using a deep learning architec…
Feudal Reinforcement Learning for Dialogue Management in Large Domains
Iñigo Casanueva, Paweł Budzianowski, Pei-Hao Su +4
Reinforcement learning (RL) is a promising approach to solve dialogue policy optimisation. Traditional RL algorithms, however, fail to scale to large domains due to the curse of di…
Reward-Balancing for Statistical Spoken Dialogue Systems using Multi-objective Reinforcement Learning
Stefan Ultes, Paweł Budzianowski, Iñigo Casanueva +6
Reinforcement learning is widely used for dialogue policy optimization where the reward function often consists of more than one component, e.g., the dialogue success and the dialo…
Sub-domain Modelling for Dialogue Management with Hierarchical Reinforcement Learning
Paweł Budzianowski, Stefan Ultes, Pei-Hao Su +5
Human conversation is inherently complex, often spanning many different topics/domains. This makes policy learning for dialogue systems very challenging. Standard flat reinforcemen…
Dialogue manager domain adaptation using Gaussian process reinforcement learning
Milica Gasic, Nikola Mrksic, Lina M. Rojas-Barahona +5
Spoken dialogue systems allow humans to interact with machines using natural speech. As such, they have many benefits. By using speech as the primary communication medium, a comput…