Skim-Attention: Learning to Focus via Document Layout Laura Nguyen, Thomas Scialom, Jacopo Staiano, Benjamin Piwowarski, EMNLP 2021
Skim-Attention is a new attention mechanism that takes advantage of the document layout by attending exclusively to the 2-dimensional position of the words. We propose two approaches to exploit Skim-Attention: i) Skimformer, wherein self-attention is replaced with Skim-Attention; and ii) SkimmingMask, where an attention mask is built from Skim-Attention and fed to a Transformer language model.
skimformer | HuggingFace
Setup environment as follows:
$ conda create -n skim-attention python=3.8
$ conda activate skim-attention
$ git clone ssh://git@source.recital.ai:2222/research/doc-intelligence/skim-attention.git
$ cd skim-attention
$ pip install -r requirements.txt
$ pip install -e .Pre-train Skimformer as follows:
$ srun python experiments/run_pretraining.py --output_dir path/to/output/dir \
--max_seq_length 512 \
--data_dir path/to/data/dir \
--model_type skimformer \
--hidden_layout_size 768 \
--skim_attention_head_size 64 \
--tokenizer_name bert-base-uncased \
--use_fast_tokenizer \
--contextualize_2d_positions \
--do_train \
--do_eval \
--do_predict \
--learning_rate 1e-4 \
--max_steps 10000 \
--weight_decay 0.01 \
--warmup_steps 100 \
--per_device_train_batch_size 8 \
--fp16 \
--gradient_accumulation_steps 2
To pre-train LongSkimformer, replace skimformer with longskimformer, and provide a value for the attention_window parameter.
Skim-Attention is evaluated on a downstream task, document layout analysis. We use a subset of the DocBank dataset.
To fine-tune Skimformer, run the following:
$ run python experiments/run_layout_analysis.py --output_dir path/to/output/dir \
--max_seq_length 512 \
--data_dir path/to/data/dir \
--model_type skimformer \
--model_name_or_path path/to/pretrained/model \
--tokenizer_name bert-base-uncased \
--use_fast_tokenizer \
--do_train \
--do_eval \
--do_predict \
--num_train_epochs 10 \
--learning_rate 5e-5 \
--per_device_train_batch_size 8 \
--fp16 \
--gradient_accumulation_steps 2 To plug Skimformer's layout embeddings in BERT, do as follows:
$ run python experiments/run_layout_analysis.py --output_dir path/to/output/dir \
--max_seq_length 512 \
--data_dir path/to/data/dir \
--model_type bertwithskimembed \
--skim_model_name_or_path path/to/pretrained/skimformer/model \
--core_model_type bert \
--core_model_name_or_path path/to/pretrained/bert/model \
--tokenizer_name bert-base-uncased \
--use_fast_tokenizer \
--do_train \
--do_eval \
--do_predict \
--num_train_epochs 10 \
--learning_rate 5e-5 \
--per_device_train_batch_size 8 \
--fp16 \
--gradient_accumulation_steps 2 To plug SkimmingMask in BERT or LayoutLM, do as follows:
$ run python experiments/run_layout_analysis.py --output_dir path/to/output/dir \
--max_seq_length 512 \
--data_dir path/to/data/dir \
--model_type skimmingmask \
--skim_model_name_or_path path/to/pretrained/skimformer/model \
--core_model_type <bert | layoutlm> \
--core_model_name_or_path path/to/pretrained/bert/or/layoutlm/model \
--tokenizer_name <bert-base-uncased | microsoft/layoutlm-base-uncased> \
--use_fast_tokenizer \
--top_k 128 \
--do_train \
--do_eval \
--do_predict \
--num_train_epochs 10 \
--learning_rate 5e-5 \
--per_device_train_batch_size 8 \
--fp16 \
--gradient_accumulation_steps 2 @article{nguyen2021skimattention,
title={Skim-Attention: Learning to Focus via Document Layout},
author={Laura Nguyen and Thomas Scialom and Jacopo Staiano and Benjamin Piwowarski},
journal={arXiv preprint arXiv:2109.01078}
year={2021},
}