Abstract
This paper introduces our technical solution for CCKS-2022’s task of “A Dataset of Conditional Question Answering with Multiple-Span Answers”. The solution consists of Data Analysis and Processing, Condition-Answer Extraction, Post-extraction Processing, Condition-Answer Relation Classification, and Post-classification Processing. The rule-based post-extraction and Post-classification Processing modules consist of seven cascaded modules. Because the training data of the task contains multi-domain questions and answers and is constrained, we have designed a prediction method based on the conditions, coarse-grained answers, and fine-grained answers of the fine-tuned pre-trained language model for multi-domain scenarios. Binary classification is used for relation extraction of conditions, coarse-grained answers, and fine-grained answers, and the constraint extraction method is based on rules. The proposed solution obtains an F1 value of 0.74487 on the test set (ranking 3rd), and its effectiveness in multi-domain scenarios is verified.
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Zhu, J. et al. (2022). Cascaded Solution for Multi-domain Conditional Question Answering with Multiple-Span Answers. In: Zhang, N., Wang, M., Wu, T., Hu, W., Deng, S. (eds) CCKS 2022 - Evaluation Track. CCKS 2022. Communications in Computer and Information Science, vol 1711. Springer, Singapore. https://doi.org/10.1007/978-981-19-8300-9_6
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DOI: https://doi.org/10.1007/978-981-19-8300-9_6
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