YNU-HPCC at ROCLING 2022 Shared Task: A Transformer-based Model with Focal Loss and Regularization Dropout for Chinese Healthcare Named Entity Recognition
Xiang Luo | Jin Wang | Xuejie Zhang
Proceedings of the 34th Conference on Computational Linguistics and Speech Processing (ROCLING 2022)
Named Entity Recognition (NER) is a fundamental task in information extraction that locates the mentions of named entities and classifies them in unstructured texts. Previous studies typically used hidden Markov model (HMM) and conditional random fields (CRF) for NER. To learn long-distance dependencies in text, recurrent neural networks, e.g., LSTM and GRU can extract the semantic features for each token with a sequential manner. Based on Transformers, this paper describes the contribution to ROCLING-2022 Share Task. This paper adopts a transformer-based model with focal Loss and regularization dropout. The focal loss is to overcome the uneven distribution of the label. The regularization dropout (r-drop) is to address the problem of vocabulary and descriptions that are too domain-specific. The ensemble learning is to improve the performance of the model. Comparative experiments were conducted on dev set to select the model with the best performance for submission. That is, BERT model with BiLSTM-CRF, focal loss and R-Drop has achieved the best F1-score of 0.7768 and rank the 4th place.
TCU at SemEval-2022 Task 8: A Stacking Ensemble Transformer Model for Multilingual News Article Similarity
Xiang Luo | Yanqing Niu | Boer Zhu
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
Previous studies focus on measuring the degree of similarity of textsby using traditional machine learning methods, such as Support Vector Regression (SVR). Based on Transformers, this paper describes our contribution to SemEval-2022 Task 8 Multilingual News Article Similarity. The similarity of multilingual news articles requires a regression prediction on the similarity of multilingual articles, rather than a classification for judging text similarity. This paper mainly describes the architecture of the model and how to adjust the parameters in the experiment and strengthen the generalization ability. In this paper, we implement and construct different models through transformer-based models. We applied different transformer-based models, as well as ensemble them together by using ensemble learning. To avoid the overfit, we focus on the adjustment of parameters and the increase of generalization ability in our experiments. In the last submitted contest, we achieve a score of 0.715 and rank the 21st place.