42 citations · 105 across the 6 of their papers we have counts for
10 papers · 1 filter
Human Evaluation of Conversations is an Open Problem: comparing the sensitivity of various methods for evaluating dialogue agents
Eric Michael Smith, Orion Hsu, Rebecca Qian +3
At the heart of improving conversational AI is the open problem of how to evaluate conversations. Issues with automatic metrics are well known (Liu et al., 2016, arXiv:1603.08023),…
Hi, my name is Martha: Using names to measure and mitigate bias in generative dialogue models
Eric Michael Smith, Adina Williams
All AI models are susceptible to learning biases in data that they are trained on. For generative dialogue models, being trained on real human conversations containing unbalanced g…
Multi-Modal Open-Domain Dialogue
Kurt Shuster, Eric Michael Smith, Da Ju +1
Recent work in open-domain conversational agents has demonstrated that significant improvements in model engagingness and humanness metrics can be achieved via massive scaling in b…
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…
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…
Recipes for building an open-domain chatbot
Stephen Roller, Emily Dinan, Naman Goyal +9
Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of…