241 citations · 372 across the 11 of their papers we have counts for
19 papers
Anticipating Safety Issues in E2E Conversational AI: Framework and Tooling
Emily Dinan, Gavin Abercrombie, A. Stevie Bergman +4
Over the last several years, end-to-end neural conversational agents have vastly improved in their ability to carry a chit-chat conversation with humans. However, these models are…
Recipes for Safety in Open-domain Chatbots
Jing Xu, Da Ju, Margaret Li +3
Models trained on large unlabeled corpora of human interactions will learn patterns and mimic behaviors therein, which include offensive or otherwise toxic behavior and unwanted bi…
Controlling Style in Generated Dialogue
Eric Michael Smith, Diana Gonzalez-Rico, Emily Dinan +1
Open-domain conversation models have become good at generating natural-sounding dialogue, using very large architectures with billions of trainable parameters. The vast training da…
Deploying Lifelong Open-Domain Dialogue Learning
Kurt Shuster, Jack Urbanek, Emily Dinan +2
Much of NLP research has focused on crowdsourced static datasets and the supervised learning paradigm of training once and then evaluating test performance. As argued in de Vries e…
Open-Domain Conversational Agents: Current Progress, Open Problems, and Future Directions
Stephen Roller, Y-Lan Boureau, Jason Weston +13
We present our view of what is necessary to build an engaging open-domain conversational agent: covering the qualities of such an agent, the pieces of the puzzle that have been bui…
Multi-Dimensional Gender Bias Classification
Emily Dinan, Angela Fan, Ledell Wu +3
Machine learning models are trained to find patterns in data. NLP models can inadvertently learn socially undesirable patterns when training on gender biased text. In this work, we…