Tanguy Urvoy


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.


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.


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.


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