/QA_HRDE_LTC

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

Primary LanguagePythonMIT LicenseMIT

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

[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

[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

[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/

[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

[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} 
   }