51 citations · 66 across the 6 of their papers we have counts for
11 papers
Enforcing Consistency in Weakly Supervised Semantic Parsing
Nitish Gupta, Sameer Singh, Matt Gardner
The predominant challenge in weakly supervised semantic parsing is that of spurious programs that evaluate to correct answers for the wrong reasons. Prior work uses elaborate searc…
Paired Examples as Indirect Supervision in Latent Decision Models
Nitish Gupta, Sameer Singh, Matt Gardner +1
Compositional, structured models are appealing because they explicitly decompose problems and provide interpretable intermediate outputs that give confidence that the model is not…
What do we expect from Multiple-choice QA Systems?
Krunal Shah, Nitish Gupta, Dan Roth
The recent success of machine learning systems on various QA datasets could be interpreted as a significant improvement in models' language understanding abilities. However, using…
Improving Compositional Generalization in Semantic Parsing
Inbar Oren, Jonathan Herzig, Nitish Gupta +2
Generalization of models to out-of-distribution (OOD) data has captured tremendous attention recently. Specifically, compositional generalization, i.e., whether a model generalizes…
Obtaining Faithful Interpretations from Compositional Neural Networks
Sanjay Subramanian, Ben Bogin, Nitish Gupta +4
Neural module networks (NMNs) are a popular approach for modeling compositionality: they achieve high accuracy when applied to problems in language and vision, while reflecting the…
Evaluating Models' Local Decision Boundaries via Contrast Sets
Matt Gardner, Yoav Artzi, Victoria Basmova +23
Standard test sets for supervised learning evaluate in-distribution generalization. Unfortunately, when a dataset has systematic gaps (e.g., annotation artifacts), these evaluation…