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ACL-2022 paper: Divide and Conquer: Text Semantic Matching with Disentangled Keywords and Intents.

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DC-Match

Pytorch implementation of the ACL-2022 (findings) paper: Divide and Conquer: Text Semantic Matching with Disentangled Keywords and Intents.

Environments

  • Python 3.9.7

  • pytorch 1.10.2

  • transformers 4.17.0

  • datasets 2.0.0

  • RTX 3090 GPU & Titan RTX

  • CUDA 11.4

Data

All the processed datastes used in our work are available at Google Drive or Baidu Pan (extract code: w2op), including QQP, MRPC, and Medical-SM.

Usage

  • Download raw datasets from the above data links and put them into the directory raw_data like this:

     --- raw_data
       |
       |--- medical
       |
       |--- mrpc
       |
       |--- qqp
    
  • We have tried various pre-trained models. The following models work fine with our code:

    • model names for QQP and MRPC:

      • roberta-base
      • roberta-large
      • bert-base-uncased
      • bert-large-uncased
      • albert-base-v2
      • albert-large-v2
      • microsoft/deberta-large
      • microsoft/deberta-base
      • funnel-transformer/medium
    • model names for Medical:

      • hfl/chinese-macbert-base
      • hfl/chinese-macbert-large
      • hfl/chinese-roberta-wwm-ext
      • hfl/chinese-roberta-wwm-ext-large
  • Pre-process datasets.

    PYTHONPATH=. python ./src/preprocess.py -raw_path raw_data/mrpc
    
    PYTHONPATH=. python ./src/preprocess.py -raw_path raw_data/qqp
    
    PYTHONPATH=. python ./src/preprocess.py -raw_path raw_data/medical
    
  • Training and Evaluation (Baseline)

    • MRPC
    PYTHONPATH=. python -u src/main.py \
        -baseline \
        -task mrpc \
        -model roberta-large \
        -num_labels 2 \
        -batch_size 16 \
        -accum_count 1 \
        -test_batch_size 128 \
        >> logs/mrpc.roberta_large.baseline.log
    
    • QQP
    PYTHONPATH=. python -u src/main.py \
        -baseline \
        -task qqp \
        -model roberta-large \
        -num_labels 2 \
        -batch_size 16 \
        -accum_count 4 \
        -test_batch_size 128 \
        >> logs/qqp.roberta_large.baseline.log
    
    • Medical
    PYTHONPATH=. python -u src/main.py \
        -baseline \
        -task medical \
        -model hfl/chinese-roberta-wwm-ext-large \
        -num_labels 3 \
        -batch_size 16 \
        -accum_count 4 \
        -test_batch_size 128 \
        >> logs/medical.roberta_large.baseline.log
    
  • Training and Evaluation (DC-Match)

    • MRPC
    PYTHONPATH=. python -u src/main.py \
        -task mrpc \
        -model roberta-large \
        -num_labels 2 \
        -batch_size 16 \
        -accum_count 1 \
        -test_batch_size 128 \
        >> logs/mrpc.roberta_large.log
    
    • QQP
    PYTHONPATH=. python -u src/main.py \
        -task qqp \
        -model roberta-large \
        -num_labels 2 \
        -batch_size 16 \
        -accum_count 4 \
        -test_batch_size 128 \
        >> logs/qqp.roberta_large.log
    
    • Medical
    PYTHONPATH=. python -u src/main.py \
        -task medical \
        -model hfl/chinese-roberta-wwm-ext-large \
        -num_labels 3 \
        -batch_size 16 \
        -accum_count 4 \
        -test_batch_size 128 \
        >> logs/medical.roberta_large.log
    

Citation

@article{zou2022divide,
         title={Divide and Conquer: Text Semantic Matching with Disentangled Keywords and Intents},
         author={Zou, Yicheng and Liu, Hongwei and Gui, Tao and Wang, Junzhe and Zhang, Qi and Tang, Meng and Li, Haixiang and Wang, Daniel},
         journal={arXiv preprint arXiv:2203.02898},
         year={2022}
}

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