  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. However, more than 20% of relational tables on the web
have 20 or more rows (Cafarella et al., 2008), and these large tables present a
challenge for current Transformer models, which are typically limited to 512
tokens. Here we propose MATE, a novel Transformer architecture designed to
model the structure of web tables. MATE uses sparse attention in a way that
allows heads to efficiently attend to either rows or columns in a table. This
architecture scales linearly with respect to speed and memory, and can handle
documents containing more than 8000 tokens with current accelerators. MATE also
has a more appropriate inductive bias for tabular data, and sets a new
state-of-the-art for three table reasoning datasets. For HybridQA (Chen et al.,
2020b), a dataset that involves large documents containing tables, we improve
the best prior result by 19 points.

Computation and Language;Artificial Intelligence;Information Retrieval;Machine Learning