Evaluating a How-to Tip Machine Comprehension Model with QA Examples collected from a Community QA Site
Evaluating a How-to Tip Machine Comprehension Model with QA Examples collected from a Community QA Site
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发表时间：
2021
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通讯作者：
Tingxuan Li;Shuting Bai;T. Utsuro;Fuzhu Zhu
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作者：
Tingxuan Li;Shuting Bai;T. Utsuro;Fuzhu Zhu
在事实问答（QA）领域，众所周知，最先进的技术已经达到了与人类相当的准确性。然而，在非事实QA领域，只有有限数量的数据集用于训练QA模型。因此，在非事实QA领域，Chen等人（2020）开发了一个用于训练日本提示QA模型的数据集。虽然可以表明，训练的日语提示QA模型优于factoid QA模型，本文进一步旨在回答与日常生活更密切相关的提示问题。具体来说，我们从社区QA站点收集社区QA示例，然后将经过训练的日语提示QA模型应用于这些社区QA示例。评估结果表明，经过训练的提示QA模型在对这些社区QA示例进行测试时优于factoid QA模型。
In the field of factoid question answering (QA), it is known that the state-of-the-art technology has achieved an accuracy comparable to human. However, in the area of non-factoid QA, there are only limited numbers of datasets for training QA models. So within the field of the non-factoid QA, Chen et al. (2020) developed a dataset for training Japanese tip QA models. Although it can be shown that the trained Japanese tip QA model outperforms the factoid QA model, this paper further aims at answering tip questions more closely re-lated to daily lives. Specifically, we collect community QA examples from a community QA site and then apply the trained Japanese tip QA model to those community QA examples. Evaluation results show that the trained tip QA model outperforms the factoid QA model when testing against those community QA examples.