Reimplementation of the paper `Human Attention Maps for Text Classification: Do Humans and Neural Networks Focus on the Same Words? (ACL2020)`

Overview

Human Attention for Text Classification

ACL2020 2020.acl-main.419 Code style: black Powered by AllenNLP

Re-implementation of the paper Human Attention Maps for Text Classification: Do Humans and Neural Networks Focus on the Same Words? (ACL2020).

Install requirements

$ poetry install

Download and Split Yelp dataset

Download from Yelp.com

Split the dataset

  • The Yelp dataset is so large that it is divided into subsets in advance.
    • After that, we can get tng.jsonl, val.jsonl, and tst.jsonl from data directory.
$ allennlp split-dataset \
    --input-file data/yelp_academic_dataset_review.json \
    --output-dir data/ \
    --tng-ratio 0.8 \
    --val-ratio 0.1 \
    --tst_ratio 0.1

Preprocess HAM dataset

$ allennlp preprocess-ham-dataset \
    --ham-dataset-dir data/ham-dataset/raw_data/ \
    --output-dir data/

Train RNN model

$ CUDA_VISIBLE_DEVICES=0 allennlp train config/base.jsonnet -s outputs -o '{"trainer": {"cuda_device": 0}}'

Reference

  • Sen, Cansu, et al. "Human Attention Maps for Text Classification: Do Humans and Neural Networks Focus on the Same Words?." Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020.
Owner
Shunsuke KITADA
Ph.D student working on deep learning-based natural language processing, computer vision, computational advertising.
Shunsuke KITADA
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