Chao-Yi Chen


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NCUEE-NLP at SemEval-2023 Task 7: Ensemble Biomedical LinkBERT Transformers in Multi-evidence Natural Language Inference for Clinical Trial Data
Chao-Yi Chen | Kao-Yuan Tien | Yuan-Hao Cheng | Lung-Hao Lee
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)

This study describes the model design of the NCUEE-NLP system for the SemEval-2023 NLI4CT task that focuses on multi-evidence natural language inference for clinical trial data. We use the LinkBERT transformer in the biomedical domain (denoted as BioLinkBERT) as our main system architecture. First, a set of sentences in clinical trial reports is extracted as evidence for premise-statement inference. This identified evidence is then used to determine the inference relation (i.e., entailment or contradiction). Finally, a soft voting ensemble mechanism is applied to enhance the system performance. For Subtask 1 on textual entailment, our best submission had an F1-score of 0.7091, ranking sixth among all 30 participating teams. For Subtask 2 on evidence retrieval, our best result obtained an F1-score of 0.7940, ranking ninth of 19 submissions.

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Overview of the ROCLING 2023 Shared Task for Chinese Multi-genre Named Entity Recognition in the Healthcare Domain
Lung-Hao Lee | Tzu-Mi Lin | Chao-Yi Chen
Proceedings of the 35th Conference on Computational Linguistics and Speech Processing (ROCLING 2023)

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NCUEE-NLP at BioLaySumm Task 2: Readability-Controlled Summarization of Biomedical Articles Using the PRIMERA Models
Chao-Yi Chen | Jen-Hao Yang | Lung-Hao Lee
The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks

This study describes the model design of the NCUEE-NLP system for BioLaySumm Task 2 at the BioNLP 2023 workshop. We separately fine-tune pretrained PRIMERA models to independently generate technical abstracts and lay summaries of biomedical articles. A total of seven evaluation metrics across three criteria were used to compare system performance. Our best submission was ranked first for relevance, second for readability, and fourth for factuality, tying first for overall performance.


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NCUEE-NLP@SMM4H’22: Classification of Self-reported Chronic Stress on Twitter Using Ensemble Pre-trained Transformer Models
Tzu-Mi Lin | Chao-Yi Chen | Yu-Wen Tzeng | Lung-Hao Lee
Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task

This study describes our proposed system design for the SMM4H 2022 Task 8. We fine-tune the BERT, RoBERTa, ALBERT, XLNet and ELECTRA transformers and their connecting classifiers. Each transformer model is regarded as a standalone method to detect tweets that self-reported chronic stress. The final output classification result is then combined using the majority voting ensemble mechanism. Experimental results indicate that our approach achieved a best F1-score of 0.73 over the positive class.

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Overview of the ROCLING 2022 Shared Task for Chinese Healthcare Named Entity Recognition
Lung-Hao Lee | Chao-Yi Chen | Liang-Chih Yu | Yuen-Hsien Tseng
Proceedings of the 34th Conference on Computational Linguistics and Speech Processing (ROCLING 2022)

This paper describes the ROCLING-2022 shared task for Chinese healthcare named entity recognition, including task description, data preparation, performance metrics, and evaluation results. Among ten registered teams, seven participating teams submitted a total of 20 runs. This shared task reveals present NLP techniques for dealing with Chinese named entity recognition in the healthcare domain. All data sets with gold standards and evaluation scripts used in this shared task are publicly available for future research.


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Generative Adversarial Networks based on Mixed-Attentions for Citation Intent Classification in Scientific Publications
Yuh-Shyang Wang | Chao-Yi Chen | Lung-Hao Lee
Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)

We propose the mixed-attention-based Generative Adversarial Network (named maGAN), and apply it for citation intent classification in scientific publication. We select domain-specific training data, propose a mixed-attention mechanism, and employ generative adversarial network architecture for pre-training language model and fine-tuning to the downstream multi-class classification task. Experiments were conducted on the SciCite datasets to compare model performance. Our proposed maGAN model achieved the best Macro-F1 of 0.8532.

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NCU-NLP at ROCLING-2021 Shared Task: Using MacBERT Transformers for Dimensional Sentiment Analysis
Man-Chen Hung | Chao-Yi Chen | Pin-Jung Chen | Lung-Hao Lee
Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)

We use the MacBERT transformers and fine-tune them to ROCLING-2021 shared tasks using the CVAT and CVAS data. We compare the performance of MacBERT with the other two transformers BERT and RoBERTa in the valence and arousal dimensions, respectively. MAE and correlation coefficient (r) were used as evaluation metrics. On ROCLING-2021 test set, our used MacBERT model achieves 0.611 of MAE and 0.904 of r in the valence dimensions; and 0.938 of MAE and 0.549 of r in the arousal dimension.