Publication

Training Millions of Personalized Dialogue Agents

Empirical Methods in Natural Language Processing (EMNLP)


Abstract

Current dialogue systems are not very engaging for users, especially when trained end-to-end without relying on proactive reengaging scripted strategies. Zhang et al. (2018) showed that the engagement level of end-to-end dialogue models increases when conditioning them on text personas providing some personalized back-story to the model. However, the dataset used in (Zhang et al., 2018) is synthetic and of limited size as it contains around 1k different personas. In this paper we introduce a new dataset providing 5 million personas and 700 million persona-based dialogues. Our experiments show that, at this scale, training using personas still improves the performance of end-to-end systems. In addition, we show that other tasks benefit from the wide coverage of our dataset by fine-tuning our model on the data from (Zhang et al., 2018) and achieving state-of-the-art results.

Related Publications

All Publications

EACL - April 18, 2021

Co-evolution of language and agents in referential games

Gautier Dagan, Dieuwke Hupkes, Elia Bruni

PPSN - September 2, 2020

Variance Reduction for Better Sampling in Continuous Domains

Laurent Meunier, Carola Doerr, Jeremy Rapin, Olivier Teytaud

ACL - May 2, 2021

MLQA: Evaluating Cross-lingual Extractive Question Answering

Patrick Lewis, Barlas Oğuz, Ruty Rinott, Sebastian Riedel, Holger Schwenk

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy