Linjing Liu


2023

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EARA: Improving Biomedical Semantic Textual Similarity with Entity-Aligned Attention and Retrieval Augmentation
Ying Xiong | Xin Yang | Linjing Liu | Ka-Chun Wong | Qingcai Chen | Yang Xiang | Buzhou Tang
Findings of the Association for Computational Linguistics: EMNLP 2023

Measuring Semantic Textual Similarity (STS) is a fundamental task in biomedical text processing, which aims at quantifying the similarity between two input biomedical sentences. Unfortunately, the STS datasets in the biomedical domain are relatively smaller but more complex in semantics than common domain, often leading to overfitting issues and insufficient text representation even based on Pre-trained Language Models (PLMs) due to too many biomedical entities. In this paper, we propose EARA, an entity-aligned, attention-based and retrieval-augmented PLMs. Our proposed EARA first aligns the same type of fine-grained entity information in each sentence pair with an entity alignment matrix. Then, EARA regularizes the attention mechanism with an entity alignment matrix with an auxiliary loss. Finally, we add a retrieval module that retrieves similar instances to expand the scope of entity pairs and improve the model’s generalization. The comprehensive experiments reflect that EARA can achieve state-of-the-art performance on both in-domain and out-of-domain datasets. Source code is available.