Hongkuan Zhang


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Cross-Modal Similarity-Based Curriculum Learning for Image Captioning
Hongkuan Zhang | Saku Sugawara | Akiko Aizawa | Lei Zhou | Ryohei Sasano | Koichi Takeda
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Image captioning models require the high-level generalization ability to describe the contents of various images in words. Most existing approaches treat the image–caption pairs equally in their training without considering the differences in their learning difficulties. Several image captioning approaches introduce curriculum learning methods that present training data with increasing levels of difficulty. However, their difficulty measurements are either based on domain-specific features or prior model training. In this paper, we propose a simple yet efficient difficulty measurement for image captioning using cross-modal similarity calculated by a pretrained vision–language model. Experiments on the COCO and Flickr30k datasets show that our proposed approach achieves superior performance and competitive convergence speed to baselines without requiring heuristics or incurring additional training costs. Moreover, the higher model performance on difficult examples and unseen data also demonstrates the generalization ability.


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基于BERT的端到端中文篇章事件抽取(A BERT-based End-to-End Model for Chinese Document-level Event Extraction)
Hongkuan Zhang (张洪宽) | Hui Song (宋晖) | Shuyi Wang (王舒怡) | Bo Xu (徐波)
Proceedings of the 19th Chinese National Conference on Computational Linguistics


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Development of a Medical Incident Report Corpus with Intention and Factuality Annotation
Hongkuan Zhang | Ryohei Sasano | Koichi Takeda | Zoie Shui-Yee Wong
Proceedings of the Twelfth Language Resources and Evaluation Conference

Medical incident reports (MIRs) are documents that record what happened in a medical incident. A typical MIR consists of two sections: a structured categorical part and an unstructured text part. Most texts in MIRs describe what medication was intended to be given and what was actually given, because what happened in an incident is largely due to discrepancies between intended and actual medications. Recognizing the intention of clinicians and the factuality of medication is essential to understand the causes of medical incidents and avoid similar incidents in the future. Therefore, we are developing an MIR corpus with annotation of intention and factuality as well as of medication entities and their relations. In this paper, we present our annotation scheme with respect to the definition of medication entities that we take into account, the method to annotate the relations between entities, and the details of the intention and factuality annotation. We then report the annotated corpus consisting of 349 Japanese medical incident reports.