  The Transformer-based encoder-decoder architecture has recently made
significant advances in recognizing handwritten mathematical expressions.
However, the transformer model still suffers from the lack of coverage problem,
making its expression recognition rate (ExpRate) inferior to its RNN
counterpart. Coverage information, which records the alignment information of
the past steps, has proven effective in the RNN models. In this paper, we
propose CoMER, a model that adopts the coverage information in the transformer
decoder. Specifically, we propose a novel Attention Refinement Module (ARM) to
refine the attention weights with past alignment information without hurting
its parallelism. Furthermore, we take coverage information to the extreme by
proposing self-coverage and cross-coverage, which utilize the past alignment
information from the current and previous layers. Experiments show that CoMER
improves the ExpRate by 0.61%/2.09%/1.59% compared to the current
state-of-the-art model, and reaches 59.33%/59.81%/62.97% on the CROHME
2014/2016/2019 test sets.

Computer Vision and Pattern Recognition