Tanguy Urvoy


pdf bib
Few-Shot Structured Policy Learning for Multi-Domain and Multi-Task Dialogues
Thibault Cordier | Tanguy Urvoy | Fabrice Lefèvre | Lina M. Rojas Barahona
Findings of the Association for Computational Linguistics: EACL 2023

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 are only rough approximations of human behaviour. The ability to learn from a small number of human interactions is hence crucial, especially on multi-domain and multi-task environments where the action space is large. We therefore propose to use structured policies to improve sample efficiency when learning on these kinds of environments. We also evaluate the impact of learning from human vs simulated experts. Among the different levels of structure that we tested, the graph neural networks (GNNs) show a remarkable superiority by reaching a success rate above 80% with only 50 dialogues when learning from simulated experts. They also show superiority when learning from human experts, although a performance drop was observed. We therefore suggest to concentrate future research efforts on bridging the gap between human data, simulators and automatic evaluators in dialogue frameworks.


pdf bib
Graph Neural Network Policies and Imitation Learning for Multi-Domain Task-Oriented Dialogues
Thibault Cordier | Tanguy Urvoy | Fabrice Lefèvre | Lina M. Rojas Barahona
Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue

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. The dialogue manager must therefore be able to take into account domain changes and plan over different domains/tasks in order to deal with multi-domain dialogues. However, learning with reinforcement in such context becomes difficult because the state-action dimension is larger while the reward signal remains scarce. Our experimental results suggest that structured policies based on graph neural networks combined with different degrees of imitation learning can effectively handle multi-domain dialogues. The reported experiments underline the benefit of structured policies over standard policies.


pdf bib
Neural-Driven Search-Based Paraphrase Generation
Betty Fabre | Tanguy Urvoy | Jonathan Chevelu | Damien Lolive
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

We study a search-based paraphrase generation scheme where candidate paraphrases are generated by iterated transformations from the original sentence and evaluated in terms of syntax quality, semantic distance, and lexical distance. The semantic distance is derived from BERT, and the lexical quality is based on GPT2 perplexity. To solve this multi-objective search problem, we propose two algorithms: Monte-Carlo Tree Search For Paraphrase Generation (MCPG) and Pareto Tree Search (PTS). We provide an extensive set of experiments on 5 datasets with a rigorous reproduction and validation for several state-of-the-art paraphrase generation algorithms. These experiments show that, although being non explicitly supervised, our algorithms perform well against these baselines.


pdf bib
Denoising Pre-Training and Data Augmentation Strategies for Enhanced RDF Verbalization with Transformers
Sebastien Montella | Betty Fabre | Tanguy Urvoy | Johannes Heinecke | Lina Rojas-Barahona
Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)

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 efficient way to store facts at a large scale. However, its abstract representation makes it difficult for humans to interpret. For this purpose, the WebNLG challenge aims at promoting automated RDF-to-text generation. We propose to leverage pre-trainings from augmented data with the Transformer model using a data augmentation strategy. Our experiment results show a minimum relative increases of 3.73%, 126.05% and 88.16% in BLEU score for seen categories, unseen entities and unseen categories respectively over the standard training.


pdf bib
Bandit structured prediction for learning from partial feedback in statistical machine translation
Artem Sokolov | Stefan Riezler | Tanguy Urvoy
Proceedings of Machine Translation Summit XV: Papers