Abstract
There are many different expressions for the same event. The goal of event co-reference resolution is to build a calculation model for the similarity of event expressions to achieve the unity of data. Based on the Roberta pre-trained model, aiming at the problem of unbalanced distribution of difficult and easy cases in data, the effectiveness of various methods to enhance the generalization ability of the model is explored, including different data input methods, data enhancement, different loss functions, adversarial learning, contrastive learning. The best data input and model training methods are finally selected. On the CCKS2021 event co-reference resolution task for communication field, the f1 value of single model reaches 0.80 in test dataset 1 and 0.89 in test dataset 2.
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Yu, X., Bai, X., Liao, S., Cui, H. (2022). Strategies for Enhancing Generalization Ability of Communication Event Co-reference Resolution. In: Qin, B., Wang, H., Liu, M., Zhang, J. (eds) CCKS 2021 - Evaluation Track. CCKS 2021. Communications in Computer and Information Science, vol 1553. Springer, Singapore. https://doi.org/10.1007/978-981-19-0713-5_16
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DOI: https://doi.org/10.1007/978-981-19-0713-5_16
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