Papers by Vincent Auvray
Multi-User MultiWOZ: Task-Oriented Dialogues among Multiple Users (2023.findings-emnlp)
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Yohan Jo, Xinyan Zhao, Arijit Biswas, Nikoletta Basiou, Vincent Auvray, Nikolaos Malandrakis, Angeliki Metallinou, Alexandros Potamianos
| Challenge: | a dataset of task-oriented dialogues assume conversations between the agent and one user at a time . but multi-user task-orientated dialogues are richer, containing deliberation and deliberations . a novel task is proposed to rewrite a task-focused query that retains only task-relevant information . |
| Approach: | They propose to rewrite a task-oriented chat between two users as a concise task-orientated query that retains only task-relevant information and is directly consumable by the dialogue system. |
| Outcome: | The proposed method surpasses existing models on multi-user dialogues and generalizes to unseen domains. |
Alexa Conversations: An Extensible Data-driven Approach for Building Task-oriented Dialogue Systems (2021.naacl-demos)
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Anish Acharya, Suranjit Adhikari, Sanchit Agarwal, Vincent Auvray, Nehal Belgamwar, Arijit Biswas, Shubhra Chandra, Tagyoung Chung, Maryam Fazel-Zarandi, Raefer Gabriel, Shuyang Gao, Rahul Goel, Dilek Hakkani-Tur, Jan Jezabek, Abhay Jha, Jiun-Yu Kao, Prakash Krishnan, Peter Ku, Anuj Goyal, Chien-Wei Lin, Qing Liu, Arindam Mandal, Angeliki Metallinou, Vishal Naik, Yi Pan, Shachi Paul, Vittorio Perera, Abhishek Sethi, Minmin Shen, Nikko Strom, Eddie Wang
| Challenge: | Traditional goal-oriented dialogue systems require annotations which are hard to obtain for every new domain, limiting scalability. |
| Approach: | They propose a data-driven approach to building goal-oriented dialogue systems . they use a seed dialogue simulator to generate annotated conversations instead of collecting annotations . |
| Outcome: | The proposed system improves turn-level action signature prediction accuracy by 50% . the system is scalable, extensible and data efficient . |
OodGAN: Generative Adversarial Network for Out-of-Domain Data Generation (2021.naacl-industry)
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| Challenge: | Existing models for OOD detection work with text, but they do not work directly with the text. |
| Approach: | They propose to use a sequential generative adversarial network (SeqGAN) based model to generate OOD data for a given domain automatically. |
| Outcome: | The proposed model outperforms state-of-the-art in OOD detection metrics for ROSTD and OSQ datasets. |