3 citations · 5 across the 4 of their papers we have counts for
6 papers
Augmentation by Counterfactual Explanation -- Fixing an Overconfident Classifier
Sumedha Singla, Nihal Murali, Forough Arabshahi +2
A highly accurate but overconfident model is ill-suited for deployment in critical applications such as healthcare and autonomous driving. The classification outcome should reflect…
AutoNLU: Detecting, root-causing, and fixing NLU model errors
Pooja Sethi, Denis Savenkov, Forough Arabshahi +6
Improving the quality of Natural Language Understanding (NLU) models, and more specifically, task-oriented semantic parsing models, in production is a cumbersome task. In this work…
Conversational Multi-Hop Reasoning with Neural Commonsense Knowledge and Symbolic Logic Rules
Forough Arabshahi, Jennifer Lee, Antoine Bosselut +2
One of the challenges faced by conversational agents is their inability to identify unstated presumptions of their users' commands, a task trivial for humans due to their common se…
Conversational Neuro-Symbolic Commonsense Reasoning
Forough Arabshahi, Jennifer Lee, Mikayla Gawarecki +3
In order for conversational AI systems to hold more natural and broad-ranging conversations, they will require much more commonsense, including the ability to identify unstated pre…
Compositional Generalization with Tree Stack Memory Units
Forough Arabshahi, Zhichu Lu, Pranay Mundra +2
We study compositional generalization, viz., the problem of zero-shot generalization to novel compositions of concepts in a domain. Standard neural networks fail to a large extent…
Look-up and Adapt: A One-shot Semantic Parser
Zhichu Lu, Forough Arabshahi, Igor Labutov +1
Computing devices have recently become capable of interacting with their end users via natural language. However, they can only operate within a limited "supported" domain of disco…