scikgat

Scientific Fact Extraction and Verification with KGAT (SciKGAT)

There are source codes for Adapting Open Domain Fact Extraction and Verification to COVID-FACT through In-Domain Language Modeling.

More information about the SCIFACT shared task can be found on this website.

In this part, we mainly focus on continuously training language models for the scientific domain. For more advanced retrieval technics for COVID search, please refer to our paper and OpenMatch toolkit to achieve top retrieval for COVID search.

Requirements

• requirments.txt

Use Our Codes

• We provide annotations and some scripts for users in the script data folder.
• Inference with our best pipeline, and you can get the evaluation results:

bash script/inference.sh

bash script/eval.sh

• Train the rationale selection model with the same code with our baseline:

bash script/train_rs.sh

• Train the KGAT model with this command:

bash script/train_kgat.sh

Continuous Training

• The continuous training is presented. We provide two methods, Mask Language Model (MLM) and Rationale Prediction (PR). For MLM, please go to mlm_models for more information. The other one is the same with rationale selection training. We can warm up from these models.

Our Results

• All our experimental results are provided in the prediction folder.
• We provide scripts for training, inference, and eval in the script folder.

Evaluation for SCIKGAT

Overall performance on the development set:

	Sentence Level			Abstract Level
Model	Prec.	Rec.	F1	Prec.	Rec.	F1
RoBERTa (Large)	46.51	38.25	41.98	53.30	46.41	49.62
KGAT	57.07	31.97	40.98	72.73	38.28	50.16
SciKGAT (w.A)	42.07	47.81	44.76	47.66	58.37	52.47
SciKGAT (w, AR)	50	47.81	48.88	53.15	56.46	54.76
SciKGAT (Full)	74.36	39.62	51.69	84.26	43.54	57.41

Overall performance on the testing set:

	Sentence Level			Abstract Level
Model	Prec.	Rec.	F1	Prec.	Rec.	F1
RoBERTa (Large)	38.6	40.5	39.5	46.6	46.4	46.5
SciKGAT (w.A)	40.5	48.38	44.09	47.06	57.66	51.82
SciKGAT (w, AR)	41.67	45.95	43.7	47.47	54.96	50.94
SciKGAT (Full)	61.15	42.97	50.48	76.09	47.3	58.3

We also conduct some ablation studies about abstract retrieval and rationale selection for further work (These parts are omitted in our paper because of the limited space). We find that the abstract retrieval can be improved with a single SciBERT (MLM), and here are the results. In this experiment, we use SciBERT (MLM) to rerank the top 100 TF-IDF retrieved abstract and then leverage the following steps of our baseline (RoBERTa large) to do rationale selection and claim label prediction.

Model	Ranking Acc.		Abstract Level			Sentence Level
	Hit one	Hit all	Precision	Recall	F1	Precision	Recall	F1
TF-IDF	84.67	83.33	53.30	46.41	49.62	46.51	38.25	41.98
w. SciBERT	94.67	93.00	48.18	56.94	52.19	42.09	47.27	44.53
w. SciBERT (MLM)	95.33	93.67	47.66	58.37	52.47	42.07	47.81	44.76

Also, we have ablation studies of rationale selection:

Model	Ranking Acc.			Sentence Level			Abstract Level
	Precision	Recall	F1	Precision	Recall	F1	Precision	Recall	F1
SciBERT	36.90	65.03	47.08	43.22	46.99	45.03	48.94	55.02	51.80
SciBERT (MLM)	43.73	60.93	50.91	50.00	47.81	48.88	53.15	56.46	54.76
RoBERTa-Base	37.56	61.48	46.63	43.64	45.90	44.74	46.06	53.11	49.33
RoBERTa-Base (MLM)	29.82	61.75	40.21	41.45	48.36	44.64	45.02	54.07	49.13
RoBERTa-Large	36.78	64.21	46.77	42.07	47.81	44.76	47.66	58.37	52.47
RoBERTa-Large (MLM)	38.44	63.11	47.78	42.93	46.45	44.62	47.03	53.11	49.89

Citation

@inproceedings{liu2020adapting,
    title = {Adapting Open Domain Fact Extraction and Verification to COVID-FACT through In-Domain Language Modeling},
    author = {Liu, Zhenghao and Xiong, Chenyan and Dai, Zhuyun and Sun, Si and Sun, Maosong and Liu, Zhiyuan},
    booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2020},
   year={2020}
}

Contact

If you have questions, suggestions, and bug reports, please email:

[email protected]