42 citations · 69 across the 6 of their papers we have counts for
8 papers
Reason first, then respond: Modular Generation for Knowledge-infused Dialogue
Leonard Adolphs, Kurt Shuster, Jack Urbanek +2
Large language models can produce fluent dialogue but often hallucinate factual inaccuracies. While retrieval-augmented models help alleviate this issue, they still face a difficul…
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…
I love your chain mail! Making knights smile in a fantasy game world: Open-domain goal-oriented dialogue agents
Shrimai Prabhumoye, Margaret Li, Jack Urbanek +4
Dialogue research tends to distinguish between chit-chat and goal-oriented tasks. While the former is arguably more naturalistic and has a wider use of language, the latter has cle…
Generating Interactive Worlds with Text
Angela Fan, Jack Urbanek, Pratik Ringshia +8
Procedurally generating cohesive and interesting game environments is challenging and time-consuming. In order for the relationships between the game elements to be natural, common…
Queens are Powerful too: Mitigating Gender Bias in Dialogue Generation
Emily Dinan, Angela Fan, Adina Williams +3
Models often easily learn biases present in the training data, and their predictions directly reflect this bias. We analyze gender bias in dialogue data, and examine how this bias…