Rectifying and Discriminating Hard Negatives for Biomedical Retrieval Question Answering

Retrieval Question Answering (ReQA) is a pivotal task in biomedical natural language processing, where the bi-encoders is a commonly employed solution due to its efficiency in retrieving answers from large candidate pools. However, bi-encoders falls short in capturing fine-grained interactions between questions and answers, a limitation that is even more pronounced in the biomedical field due to the inadequate model training caused by data scarcity. In recent developments, researchers have introduced hard in-batch negative sampling to enhance performance by enriching the training process with informative instances. However, the utilization of hard negative samples introduces new challenges: the emergence of false negatives that can mislead the training process, and the suboptimal quality of sentence embeddings further hampers the bi-encoderss ability to discriminate hard negative samples effectively. To address these challenges, we propose the Rectifying and Discriminating Hard negatives (RigHt) framework. RigHt rectifies negative sample labels through cross-encoder interaction, mitigating the impact of false negatives. Simultaneously, it enhances the bi-encoders to discriminate hard negative samples by refining disentangled sentence embeddings. Extensive experimental results on five datasets substantiate the efficacy of our proposed approach in enhancing the training of the bi-encoders model with hard negative samples.