14 citations · 30 across the 7 of their papers we have counts for
11 papers
Fixing Model Bugs with Natural Language Patches
Shikhar Murty, Christopher D. Manning, Scott Lundberg +1
Current approaches for fixing systematic problems in NLP models (e.g. regex patches, finetuning on more data) are either brittle, or labor-intensive and liable to shortcuts. In con…
On Measuring the Intrinsic Few-Shot Hardness of Datasets
Xinran Zhao, Shikhar Murty, Christopher D. Manning
While advances in pre-training have led to dramatic improvements in few-shot learning of NLP tasks, there is limited understanding of what drives successful few-shot adaptation in…
Characterizing Intrinsic Compositionality in Transformers with Tree Projections
Shikhar Murty, Pratyusha Sharma, Jacob Andreas +1
When trained on language data, do transformers learn some arbitrary computation that utilizes the full capacity of the architecture or do they learn a simpler, tree-like computatio…
ExpBERT: Representation Engineering with Natural Language Explanations
Shikhar Murty, Pang Wei Koh, Percy Liang
Suppose we want to specify the inductive bias that married couples typically go on honeymoons for the task of extracting pairs of spouses from text. In this paper, we allow model d…
CLOSURE: Assessing Systematic Generalization of CLEVR Models
Dzmitry Bahdanau, Harm de Vries, Timothy J. O'Donnell +4
The CLEVR dataset of natural-looking questions about 3D-rendered scenes has recently received much attention from the research community. A number of models have been proposed for…
Embedded-State Latent Conditional Random Fields for Sequence Labeling
Dung Thai, Sree Harsha Ramesh, Shikhar Murty +2
Complex textual information extraction tasks are often posed as sequence labeling or \emph{shallow parsing}, where fields are extracted using local labels made consistent through p…