DyKGChat

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)

Setup

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

$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

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

Name		Name	Last commit message	Last commit date
Latest commit History 4 Commits
data		data
model_ckpts		model_ckpts
models		models
Qadpt_model.png		Qadpt_model.png
Readme.md		Readme.md
args.py		args.py
batch_utils.py		batch_utils.py
change_test_funcs.py		change_test_funcs.py
data_utils.py		data_utils.py
friends.sh		friends.sh
hgzhz.sh		hgzhz.sh
hgzhz_v1_0.sh		hgzhz_v1_0.sh
knowledge_graph.py		knowledge_graph.py
main.py		main.py
main_model.py		main_model.py
run.sh		run.sh
test_funcs.py		test_funcs.py