134 citations · 134 across the 2 of their papers we have counts for
8 papers
Human-centric Dialog Training via Offline Reinforcement Learning
Natasha Jaques, Judy Hanwen Shen, Asma Ghandeharioun +5
How can we train a dialog model to produce better conversations by learning from human feedback, without the risk of humans teaching it harmful chat behaviors? We start by hosting…
Characterizing Sources of Uncertainty to Proxy Calibration and Disambiguate Annotator and Data Bias
Asma Ghandeharioun, Brian Eoff, Brendan Jou +1
Supporting model interpretability for complex phenomena where annotators can legitimately disagree, such as emotion recognition, is a challenging machine learning task. In this wor…
Hierarchical Reinforcement Learning for Open-Domain Dialog
Abdelrhman Saleh, Natasha Jaques, Asma Ghandeharioun +2
Open-domain dialog generation is a challenging problem; maximum likelihood training can lead to repetitive outputs, models have difficulty tracking long-term conversational goals,…
Towards Understanding Emotional Intelligence for Behavior Change Chatbots
Asma Ghandeharioun, Daniel McDuff, Mary Czerwinski +1
A natural conversational interface that allows longitudinal symptom tracking would be extremely valuable in health/wellness applications. However, the task of designing emotionally…
Engineering Music to Slow Breathing and Invite Relaxed Physiology
Grace Leslie, Asma Ghandeharioun, Diane Y. Zhou +1
We engineered an interactive music system that influences a user's breathing rate to induce a relaxation response. This system generates ambient music containing periodic shifts in…
Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog
Natasha Jaques, Asma Ghandeharioun, Judy Hanwen Shen +5
Most deep reinforcement learning (RL) systems are not able to learn effectively from off-policy data, especially if they cannot explore online in the environment. These are critica…