An evaluation bentchmark for classical Chinese. This work has been accepted by Findings of ACL 2023.

Classical Chinese is a treasure of the entire human cultural history. We contribute this work with the hope of helping the entire community to be more prosperous. Our work will be an open, community-driven project which improves with the advancement of technology.

We hope more people join in to make this benchmark better and more useful.

Leader-board

Online leader-board

See WYWEB on CADAL for the official leader-board.

Main

Models	Avg.	PUNC	GLNER	GJC	FSPC	TLC	Xuci	WYWRC	IRC
Human	88.0	92.4	94.3	90.3	80.0	89.0	85.3	80.0	92.3
DeBERTa-base	75.9	83.3	86.7	85.2	61.1	86.7	72.4	45.1	86.7
GuwenBERT-base	72.9	82.5	82.8	84.8	61.3	85.1	71.7	28.0	86.8
GuwenBERT-large	75.6	83.1	86.1	84.9	58.5	87.6	73.4	44.4	87.8
GuwenBERT-base-fs	74.6	82.9	84.8	84.2	61.0	86.7	70.0	42.1	85.3
RoBERTa-CCBC	74.5	82.5	84.7	84.5	59.5	85.0	73.2	40.7	86.1
RoBERTa-CCLC	75.3	82.8	86.1	84.7	58.6	87.1	74.9	41.0	86.9
SikuBERT	73.7	80.8	82.8	82.2	60.9	82.4	70.4	44.0	85.8
SikuRoBERTa	73.5	81.4	82.8	82.5	62.2	83.8	68.5	41.0	85.8
RoBERTa-wwm-ext	72.1	78.8	79.8	81.3	59.2	78.3	71.0	42.1	86.2

WYWMT

Model	BLEU	chrF2	TER	ROUGE-1	ROUGE-2	ROUGE-L
Human	45.6	44.2	34.4	77.4	50.7	76.2
guwenbert-base	40.1	38.1	37.5	72.5	46.0	70.3
guwenbert-large	38.8	37.2	38.1	70.1	43.7	67.7
guwenbert-base-fs	36.3	35.2	39.2	68.3	41.2	65.7
roberta-CCBC	39.1	37.1	36.8	71.4	44.9	69.3
roberta-CCLC	39.8	38.0	36.4	71.6	45.3	69.3
SikuBERT	38.8	36.2	37.9	72.0	45.5	69.8
SikuRoBERTa	39.1	36.5	37.7	72.2	45.7	70.0
DeBERTa-base	39.5	37.8	35.9	71.9	44.2	68.7
Roberta-wwm-ext	38.0	35.8	39.1	69.9	43.2	66.7

How to test new models?

This is an evaluation benchmark for classical Chinese NLP providing several tasks. Researchers could quickly evaluate pre-trained language models with a few lines of code using the evaluation toolkit.

Quick Run The Base Line

python run.py  \
                --tag wywweb \
                --do_train \
                --max_seq_len 512 \
                --dump 1000 \
                --task_name GJCTask \
                --data_dir data/tasks/gjc \
                --output_dir output/deberta/GJCTask \
                --num_train_epochs 6 \
                --model_dir_or_name bozhou/DeBERTa-base \
                --learning_rate 2e-5 \
                --train_batch_size 48 \
                --fp16 True \
                --workers 4 \
                --warmup 1000 

Test your model and contact us to update the leader board.

• test your model on every task.
• get the best dev set score, use this model to evaluate test set.
• send result of the test set to us.
• maintainers validate the result and then update the leader board.

Task Description

Task	Train	Dev	Test	Description	Metric	Source
PUNC	90k	20k	20k	Sequence labeling	F1	Authoritative Texts
TLC	28k	6k	6k	Sentence classification	Accuracy	Ancient prose
GJC	100k	20k	20k	Sentence classification	Accuracy	Daizhige
XuCi	800	200	200	Token similarity	Accuracy	Exam papers
WYWRC	3k	500	500	Reading comprehension	Accuracy	Exam papers
IRC	3k	1k	1k	Reading comprehension	Accuracy	Exam papers
WYWMT	20k	3k	3k	Machine Translation	BLEU	online
GLNER	80k	18k	18k	Sequence labeling	F1	\citet{GULIAN2020}
FSPC	3000	1000	1000	Sentence classification	Accuracy	THU-FSPC

Cite us

@inproceedings{zhou-etal-2023-wyweb,
    title = "{WYWEB}: A {NLP} Evaluation Benchmark For Classical {C}hinese",
    author = "Zhou, Bo  and
      Chen, Qianglong  and
      Wang, Tianyu  and
      Zhong, Xiaomi  and
      Zhang, Yin",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.204",
    doi = "10.18653/v1/2023.findings-acl.204",
    pages = "3294--3319"
}