Event Coreference Resolution based on Convolutional Siamese network and Circle Loss

The purpose of the cross-document event coreference resolution task is to solve multi-text processing. However, cross-document coreference resolution has not been fully explored. None of the existing methods explore the situation where the predicted coreference scores are also low when similar events have a low semantic similarity. To solve such a problem, in this paper, we propose a novel model by focusing on event classes with low event semantic similarity. Specifically, by building the Siamese network framework to enhance the feature representation, we convert the event-to-node into event-to-domain. In addition, by changing the form of the problem from traditional pairwise to listwise, the distance between coreference event nodes is greatly reduced, and the common coreference event resolution is solved. Our model only employs a very small dataset to annotate information, increases the semantic distance of these event pairs in the new vector space, and greatly improves the effect of event clustering. For the cross-document coreference resolution dataset ECB+, our model achieves better results than other models that have not been fine-tuned on more datasets or language models.