RESEARCH

NLP

Personalizing Dialogue Agents: I have a dog, do you have pets too?

July 15, 2018

Abstract

Chit-chat models are known to have several problems: they lack specificity, do not display a consistent personality and are often not very captivating. In this work we present the task of making chit-chat more engaging by conditioning on profile information. We collect data and train models to (i) condition on their given profile information; and (ii) information about the person they are talking to, resulting in improved dialogues, as measured by next utterance prediction. Since (ii) is initially unknown, our model is trained to engage its partner with personal topics, and we show the resulting dialogue can be used to predict profile information about the interlocutors.

Download the Paper

AUTHORS

Written by

Jason Weston

Arthur Szlam

Douwe Kiela

Emily Dinan

Jack Urbanek

Saizheng Zhang

Publisher

ACL

Related Publications

October 04, 2024

HUMAN & MACHINE INTELLIGENCE

CONVERSATIONAL AI

Beyond Turn-Based Interfaces: Synchronous LLMs as Full-Duplex Dialogue Agents

Bandhav Veluri, Benjamin Peloquin, Bokai Yu, Hongyu Gong, Shyam Gollakota

October 04, 2024

October 03, 2024

NLP

BLASER 2.0: a metric for evaluation and quality estimation of massively multilingual speech and text translation

David Dale, Marta R. Costa-jussa

October 03, 2024

September 26, 2024

SPEECH & AUDIO

NLP

Unveiling the Role of Pretraining in Direct Speech Translation

Belen Alastruey, Gerard I. Gállego, Marta R. Costa-jussa

September 26, 2024

September 05, 2024

CONVERSATIONAL AI

NLP

Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model

Chunting Zhou, Lili Yu, Arun Babu, Kushal Tirumala, Michihiro Yasunaga, Leonid Shamis, Jacob Kahn, Luke Zettlemoyer, Omer Levy, Xuezhe Ma

September 05, 2024

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.