zxh-hub/qa_hrde_ltc

TensorFlow implementation of "Learning to Rank Question-Answer Pairs using Hierarchical Recurrent Encoder with Latent Topic Clustering," NAACL-18

This repository contains the source code & data corpus for the models used in the following paper,

Learning to Rank Question-Answer Pairs using Hierarchical Recurrent Encoder with Latent Topic Clustering, NAACL-18, paper

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[requirements]

tensorflow==1.14 (tested)
python==2.7

[download dataset]

• data corpus is available from "releases" tab
• place each data corpus into following path of the project

/ data / ubuntu_v1 /
       / ubuntu_v2 /
       / samsungQA /

• Note that ubuntu_v1/v2 are originally from following github repository. ubuntu-v1, ubuntu-v2

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[source code path]

/ data           : contains dataset (ubuntu v1/v2, samsungQA)
/ src_ubuntu_v1  : source code for ubuntu v1 data
/ src_ubuntu_v2  : source code for ubuntu v2 data
/ src_samsungQA  : source code for samsung QA data

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[Training]

• each source code folder contains training script << for example >>

/src_ubunutu_v1/
./run_RDE.sh      : train ubuntu_v1 dataset with RDE model
./run_RDE_LTC.sh  : train ubuntu_v1 dataset with RDE-LTC model
./run_HRDE.sh     : train ubuntu_v1 dataset with HRDE model
./run_HRDE_LTC.sh : train ubuntu_v1 dataset with HRDE-LTC model

• best model will be stored in save folder << for example >>

/src_ubunutu_v1/save/

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[Inference]

• each source code folder contains inference code
  << execution example >> /src_ubunutu_v1/

 python eval_RDE.py       : inference ubuntu_v1 testset with RDE model
 python eval_RDE_LTC.py   : inference ubuntu_v1 testset with RDE-LTC model
 python eval_HRDE.py      : inference ubuntu_v1 testset with HRDE model
 python eval_HRDE_LTC.py  : inference ubuntu_v1 testset with HRDE-LTC model

• inference code use saved model in 'save' folder
• inference result will be stored in 'save' folder << example >>

/src_ubunutu_v1/save/result_RDE.txt

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[cite]

• Please cite our paper, when you use our code | dataset | model.

@inproceedings{yoon2018learning, 
   title={Learning to Rank Question-Answer Pairs Using Hierarchical Recurrent Encoder with Latent Topic Clustering}, 
   author={Yoon, Seunghyun and Shin, Joongbo and Jung, Kyomin}, 
   booktitle={Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies}, 
   volume={1},
   pages={1575--1584},
   year={2018} 
   }