Event extraction aims to recognize pre-defined event triggers and arguments from texts, which suffer from the lack of high-quality annotations. In most NLP applications, involving a large scale of synthetic training data is a practical and effective approach to alleviate the problem of data scarcity. However, when applying to the task of event extraction, recent data augmentation methods often neglect the problem of grammatical incorrectness, structure misalignment, and semantic drifting, leading to unsatisfactory performances. In order to solve these problems, we propose a denoised structure-to-text augmentation framework for event extraction (DAEE), which generates additional training data through the knowledge-based structure-to-text generation model and selects the effective subset from the generated data iteratively with a deep reinforcement learning agent. Experimental results on several datasets demonstrate that the proposed method generates more diverse text representations for event extraction and achieves comparable results with the state-of-the-art.

This is the system description of the CASIA_Unisound team for Task 1, Task 7b, and Task 8 of the sixth Social Media Mining for Health Applications (SMM4H) shared task in 2021. Targeting on deal with two shared challenges, the colloquial text and the imbalance annotation, among those tasks, we apply a customized pre-trained language model and propose various training strategies. Experimental results show the effectiveness of our system. Moreover, we got an F1-score of 0.87 in task 8, which is the highest among all participates.

The ICD coding task aims at assigning codes of the International Classification of Diseases in clinical notes. Since manual coding is very laborious and prone to errors, many methods have been proposed for the automatic ICD coding task. However, existing works either ignore the long-tail of code frequency or the noisy clinical notes. To address the above issues, we propose an Interactive Shared Representation Network with Self-Distillation Mechanism. Specifically, an interactive shared representation network targets building connections among codes while modeling the co-occurrence, consequently alleviating the long-tail problem. Moreover, to cope with the noisy text issue, we encourage the model to focus on the clinical note’s noteworthy part and extract valuable information through a self-distillation learning mechanism. Experimental results on two MIMIC datasets demonstrate the effectiveness of our method.