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Dynamic Knowledge-Grounded Dialogue Generation
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data
model_ckpts
models
Qadpt_model.png
Readme.md
args.py
batch_utils.py
change_test_funcs.py
data_utils.py
friends.sh
hgzhz.sh
hgzhz_v1_0.sh
knowledge_graph.py
main.py
main_model.py
run.sh
test_funcs.py

Readme.md

DyKGChat

The project contains the collected data and code of our paper Yi-Lin Tuan, Yun-Nung Chen, Hung-yi Lee. "DyKgChat: Benchmarking Dialogue Generation Grounding on Dynamic Knowledge Graphs", EMNLP 2019.

The extended abstract version is called Dynamic Knowledge-Grounded Dialogue Generation through Walking on the Graph.

  • our proposed approach: (Qadpt) Quick Adaptive Dynamic Knoledge-Grounded Neural Converation Model (pronouce: Q-adapt)

Qadpt

Setup

Installation (my environment)

  • python3.6
  • tensorflow r1.13
  • jieba
  • nltk3.2.5

Files

  • data/: the collected data hgzhz/ and friends/ as well as the trained TransE
  • model_ckpts/: the trained models in the paper

Usage

  • clone the repository
  • run the script run.sh
$bash run.sh <GPU_ID> <method> <model> <data> <exp_name>
  • for <GPU_ID>, check your device avalibility by nvidia-smi
  • for , choose from train, pred_acc, eval_pred_acc, ifchange
  • for , choose from seq2seq, MemNet, TAware, KAware, Qadpt
  • for , choose from friends, hgzhz_v1_0(used in our paper), hgzhz(current newest version)
  • for <exp_name>, check the directory model_ckpts

More description

  • testing method
    • pred_acc: for metrics Generated-KW, BLEU-2, distinct-n
    • eval_pred_acc: for metrics KW-Acc, KW/Generic, perplexity
    • ifchange: for change rates / accurate change rates
  • script options
    • the hops_num and change_level are required to be changed in run.sh
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