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Mutual attention model for matching QA pairs in dialogues

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JiaQiSJTU/QAmatching

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QA matching

Paper: Qi Jia, Mengxue Zhang, Shengyao Zhang, Kenny Q. Zhu: Matching Questions and Answers in Dialogues from Online Forums. ECAI 2020: 2046-2053

Requirements

  • tensorflow>=1.0
  • numpy

Data

Download the session-level and pair-level dataset from Google Drive and unzip the file under ./datafile.

The files includes:

  • The annotated dialogues which are split into train/dev/test sets by 7:1:2
    • train-700.json
    • dev-100.json
    • test-200.json
  • The reconstructed Q-NQ pairs used for our proposed model:
    • train-full.json
    • dev-full.json
    • test-full.json
  • Pretrained word embeddings:
    • word_emb_reduce.txt
    • word2idx-new.json

Getting Started

Here is the breakdown of the commands :

  • Train the model with
python train.py
  • Evaluate and interact with the model with (config.py batch_size=1)
python evaluate.py
  • Get the session predictions
python session_predictions.py
python outputPredict_True_role_sen.py

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Mutual attention model for matching QA pairs in dialogues

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