Install parlai package

python setup.py develop

Download Korean Wizard of Wikipedia (KoWoW)

Download

tar xvzf wizard_of_wikipedia_ko.tar.gz
mv wizard_of_wikipedia_ko data/

language combinations

Name	Knowledge	Utterance
ko	Korean	Korean
ke	Korean	English
ek	English	Korean
en	English	English

Test splits

Name	topic
random_split	Seen
topic_split	Unseen

training on KoWoW (ek dataset)

CUDA_VIS_DEV=0
LANG_TYPE=ek

NUM_EPOCHS=10
MODEL_TYPE=T5EndToEndTwoAgent
MODEL_NAME=mymodel

CUDA_VISIBLE_DEVICES="${CUDA_VIS_DEV}" parlai train_model -t wizard_of_wikipedia_ko:generator:topic_split --ln ${LANG_TYPE} -m projects.wizard_of_wikipedia_ko.generator.t5:${MODEL_TYPE}  -mf model_data/model/${MODEL_NAME}  --t5-model-arch "KETI-AIR/ke-t5-base" --t5-encoder-model-arch "KETI-AIR/ke-t5-small" --log-every-n-secs 10 --validation-patience 12 --validation-metric ppl --validation-metric-mode min --validation-every-n-epochs 5 -bs 4 --max_knowledge 32 --num-epochs ${NUM_EPOCHS} --tensorboard-log true --tensorboard-logdir ./model_data/tf_logs/${MODEL_NAME}

test topic split(Unseen) on KoWoW ek dataset

CUDA_VIS_DEV=0
LANG_TYPE=ek
MODEL_NAME=mymodel

CUDA_VISIBLE_DEVICES="${CUDA_VIS_DEV}" parlai eval_model -t wizard_of_wikipedia_ko:generator:topic_split -dt test --ln ${LANG_TYPE} -mf model_data/model/${MODEL_NAME} -bs 4 --inference beam --beam-size 4

Display sample - topic split(Unseen)

You can display predicted knowledge using enc_output field.

CUDA_VIS_DEV=0
LANG_TYPE=ek
MODEL_NAME=mymodel
NUM_EXAMPLES=300

CUDA_VISIBLE_DEVICES="${CUDA_VIS_DEV}" parlai display_model -t wizard_of_wikipedia_ko:generator:topic_split -dt test --ln ${LANG_TYPE} -mf ./model_data/model/${MODEL_NAME} -bs 1 --inference beam --beam-size 4 --display-add-fields checked_sentence,enc_output -n NUM_EXAMPLES

Acknowledgement

본 연구는 정부(과학기술정보통신부)의 재원으로 지원을 받아 수행된 연구입니다. (정보통신기획평가원, 2022-0-00320, 상황인지 및 사용자 이해를 통한 인공지능 기반 1:1 복합대화 기술 개발)