English | 简体中文

To reproduce the experiments, run python run.py to run.

F1 score, accuracy and the detailed results will be printed.

Running shell files: bash run.sh, and the contents of shell files are as follow:

DATA_DIR="data"

MODEL_DIR="bert-base-chinese"
OUTPUT_DIR="output/save_dict/"
PREDICT_DIR="data/"
MAX_LENGTH=128
MODEL_TYPE="PMI"

python run.py \
    --data_dir=${DATA_DIR} \
    --model_dir=${MODEL_DIR} \
    --model=${MODEL_TYPE} \
    --output_dir=${MODEL_TYPE} \
    --do_train=True \
    --max_length=${MAX_LENGTH} \
    --batch_size=16 \
    --learning_rate=1e-5 \
    --epochs=10 \
    --seed=2021

Running shell files: bash run.sh predict, and the contents of shell files are as follows:

DATA_DIR="data"

MODEL_DIR="bert-base-chinese"
OUTPUT_DIR="output/save_dict/"
PREDICT_DIR="data/"
MAX_LENGTH=128
MODEL_TYPE="PMI"


python run.py \
    --data_dir=${DATA_DIR} \
    --model_dir=${MODEL_DIR} \
    --output_dir=${OUTPUT_DIR} \
    --model=${MODEL_TYPE} \
    --max_length=${MAX_LENGTH} \
    --batch_size=16 \
    --learning_rate=1e-5 \
    --epochs=10 \
    --seed=2021

MODEL_TYPE="PMI" means the model PMI-tuning and MODEL_TYPE="kgbert" means the model KG-Bert.

The sample of dataset are putted in the data repo: train_triple.jsonl,dev_triple.jsonl.

In train_triple.jsonl, the data format is {"triple_id" : "0579","subject":"瓶装水","object":"跑步","predicate":"品类_适用_场景","salience": 0}.

In dev_triple.jsonl, the data format is {"triple_id":"0579","subject":"瓶装水","object":"跑步","predicate":"适用"}.

We evaluate the several models on the experiment dataset. We use KG-BERT as the base model and report the baselines of the task.

KG-BERT takes texts of h, r, t as input of bidirectional encoder such as BERT and computes scoring function of the triple with language model. In specific, the input of model is the concatenation of h, r, t, as [CLS] h [SEP] r [SEP] t [SEP]. The final hidden state C corresponding to [CLS] and the classification layer weights W are used to calculate the triple score.

The details of model PMI-tuning is in the paper.

We list hyper-parameters during the baseline experiments.

Common hyper-parameters

If you use the code, please cite the following paper:

@article{DBLP:journals/corr/abs-2205-10843,
  author    = {Yincen Qu and
               Ningyu Zhang and
               Hui Chen and
               Zelin Dai and
               Zezhong Xu and
               Chengming Wang and
               Xiaoyu Wang and
               Qiang Chen and
               Huajun Chen},
  title     = {Commonsense Knowledge Salience Evaluation with a Benchmark Dataset
               in E-commerce},
  journal   = {CoRR},
  volume    = {abs/2205.10843},
  year      = {2022},
  url       = {https://doi.org/10.48550/arXiv.2205.10843},
  doi       = {10.48550/arXiv.2205.10843},
  eprinttype = {arXiv},
  eprint    = {2205.10843},
  timestamp = {Mon, 30 May 2022 15:47:29 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2205-10843.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}