Abstract
Question and answer (Q&A) matching is a widely used task, and there have been many works focusing on this. Previous works tend to give an overall label indicating whether the question matches the answer. However, this method mainly relies on detecting identical or similar keywords in Q&A, which is inappropriate for medical text data. Based on a drug, patients’ questions may vary, such as usage, side effects, symptoms, and price. Thus, it is absurd to judge the answer containing the same drug as a matching answer. We argue a better solution is to judge alignments both in entity and intention aspects. To this end, we propose a novel model, which consists of two modules. Specifically, an extractor module gets matching features from text inputs, and then a discriminator module gives alignment labels in both aspects. An adversarial mechanism is designed to disentangle entity matching feature and intention matching feature, which reduces mutual interference. Experimental results show our method outperforms other baselines, including BERT. Further analysis indicates the effectiveness and interpretability of the proposed method.
This research was supported by the Guangdong Basic and Applied Basic Research Foundation (No. 2019A1515011387).
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Si, P., Deng, Q., Wang, Y., Zhong, B., Xu, J., Yang, Y. (2021). Judging Medical Q&A Alignments in Multiple Aspects. In: Fang, L., Chen, Y., Zhai, G., Wang, J., Wang, R., Dong, W. (eds) Artificial Intelligence. CICAI 2021. Lecture Notes in Computer Science(), vol 13070. Springer, Cham. https://doi.org/10.1007/978-3-030-93049-3_23
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