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cs.CL2021
MATE: Multi-view Attention for Table Transformer Efficiency
Julian Martin Eisenschlos, Maharshi Gor, Thomas Müller +1
This work presents a sparse-attention Transformer architecture for modeling documents that contain large tables. Tables are ubiquitous on the web, and are rich in information. Howe…
cs.CL2021
Toward Deconfounding the Influence of Entity Demographics for Question Answering Accuracy
Maharshi Gor, Kellie Webster, Jordan Boyd-Graber
The goal of question answering (QA) is to answer any question. However, major QA datasets have skewed distributions over gender, profession, and nationality. Despite that skew, mod…