“Biomedical Event Causal Relation Extraction (BECRE) is an important task in biomedical infor-mation extraction. Existing methods usually use pre-trained language models to learn semanticrepresentations and then predict the event causal relation. However, these methods struggle tocapture sufficient cues in biomedical texts for predicting causal relations. In this paper, we pro-pose a Path Reasoning-based Relation-aware Network (PRRN) to explore deeper cues for causalrelations using reinforcement learning. Specifically, our model reasons the relation paths betweenentity arguments of two events, namely entity relation path, which connects the two biomedicalevents through the multi-hop interactions between entities to provide richer cues for predictingevent causal relations. In PRRN, we design a path reasoning module based on reinforcementlearning and propose a novel reward function to encourage the model to focus on the length andcontextual relevance of entity relation paths. The experimental results on two datasets suggestthat PRRN brings considerable improvements over the state-of-the-art models.Introduction”

“Biomedical event representation can be applied to various language tasks. A biomedical eventoften involves multiple biomedical entities and trigger words, and the event structure is complex.However, existing research on event representation mainly focuses on the general domain. Ifmodels from the general domain are directly transferred to biomedical event representation, theresults may not be satisfactory. We argue that biomedical events can be divided into three hierar-chies, each containing unique feature information. Therefore, we propose the Triple-views EventHierarchy Model (TEHM) to enhance the quality of biomedical event representation. TEHM ex-tracts feature information from three different views and integrates them. Specifically, due to thecomplexity of biomedical events, We propose the Trigger-aware Aggregator module to handlecomplex units within biomedical events. Additionally, we annotate two similarity task datasetsin the biomedical domain using annotation standards from the general domain. Extensive exper-iments demonstrate that TEHM achieves state-of-the-art performance on biomedical similaritytasks and biomedical event casual relation extraction.Introduction”

“Event Temporal Relation Classification (ETRC) is crucial to natural language understanding. In recent years, the mainstream ETRC methods may not take advantage of lots of semantic information contained in golden temporal relation labels, which is lost by the discrete one-hot labels. To alleviate the loss of semantic information, we propose learning Temporal semantic information of the golden labels by Auxiliary Contrastive Learning (TempACL). Different from traditional contrastive learning methods, which further train the PreTrained Language Model (PTLM) with unsupervised settings before fine-tuning on target tasks, we design a supervised contrastive learning framework and make three improvements. Firstly, we design a new data augmentation method that generates augmentation data via matching templates established by us with golden labels. Secondly, we propose patient contrastive learning and design three patient strategies. Thirdly we design a label-aware contrastive learning loss function. Extensive experimental results show that our TempACL effectively adapts contrastive learning to supervised learning tasks which remain a challenge in practice. TempACL achieves new state-of-the-art results on TB-Dense and MATRES and outperforms the baseline model with up to 5.37%F1 on TB-Dense and 1.81%F1 on MATRES.”