Abstract
In Biomedical Natural Language Processing (BioNLP) tasks, such as Relation Extraction, Named Entity Recognition, and Text Classification, the scarcity of high-quality data remains a significant challenge. This limitation poisons large language models to correctly understand relationships between biological entities, such as molecules and diseases, or drug interactions, and further results in potential misinterpretation of biomedical documents. To address this issue, current approaches generally adopt the Synthetic Data Augmentation method which involves similarity computation followed by word replacement, but counterfactual data are usually generated. As a result, these methods disrupt meaningful word sets or produce sentences with meanings that deviate substantially from the original context, rendering them ineffective in improving model performance. To this end, this paper proposes a biomedical-dedicated rationale-based synthetic data augmentation method. Beyond the naive lexicon similarity, specific bio-relation similarity is measured to hold the augmented instance having a strong correlation with bio-relation instead of simply increasing the diversity of augmented data. Moreover, a multi-agents-involved reflection mechanism helps the model iteratively distinguish different usage of similar entities to escape falling into the mis-replace trap. We evaluate our method on the BLURB and BigBIO benchmark, which includes 9 common datasets spanning four major BioNLP tasks. Our experimental results demonstrate consistent performance improvements across all tasks, highlighting the effectiveness of our approach in addressing the challenges associated with data scarcity and enhancing the overall performance of biomedical NLP models.
Abstract (translated)
在生物医学自然语言处理（BioNLP）任务中，如关系抽取、命名实体识别和文本分类等，高质量数据的稀缺仍然是一个重大挑战。这一限制使得大型语言模型难以准确理解诸如分子与疾病之间或药物相互作用之间的生物学实体间的关系，并可能导致对生物医学文献的潜在误解。为解决此问题，目前的方法通常采用合成数据增强方法，包括相似性计算后进行词汇替换，但往往生成的是反事实数据。结果是这些方法破坏了有意义的词组集，或者产生了与原始语境显著不同的句子含义，从而无法有效提升模型性能。 为此，本文提出了一种基于生物医学专门理据的合成数据增强方法。该方法超越了简单的词汇相似度，在衡量特定生物关系的相似性时，确保增强后的实例具有较强的生物关系相关性，而不是简单地增加增强数据的多样性。此外，一种涉及多代理参与的反思机制帮助模型迭代区分类似实体的不同用法，从而避免落入错误替换的陷阱。 我们在BLURB和BigBIO基准测试上评估了我们的方法，这些基准包括涵盖四大类BioNLP任务的九个常见数据集。实验结果表明，在所有任务中均实现了持续的性能改进，这凸显了我们方法在应对数据稀缺挑战方面的有效性，并显著提升了生物医学自然语言处理模型的整体表现。
URL
https://arxiv.org/abs/2503.23673
