There has been a significant rise in the use of social Q&A to get answers to a variety of queries. One common problem faced by most social Q&A is how to help unskillful answerers construct well-received answers. Prior studies in answer quality assessment usually focus on ranking candidate answers for the sake of askers but show little value for the answerers. Moreover, existing work employs textural aspects and image quantity to predict answer quality, but semantic information inherent in answer images is rarely considered. To bridge the research gap, we designed an artifact, answer advisor (AA), to help answerers produce well-received answers. Our AA uses an image-text coherence measure that is obtained by integrating topic modeling with a deep learning approach. On a real-world dataset, the proposed measure can reduce the prediction error of answer popularity before the answer is actually posted on the Q&A site by 38.12%.

中文翻译：

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在社交问答网站上利用图文连贯性提高答案质量

使用社交问答来获取各种问题的答案的情况显着增加。大多数社交问答面临的一个常见问题是如何帮助不熟练的回答者构建广受欢迎的答案。先前的答案质量评估研究通常侧重于为提问者对候选答案进行排名，但对回答者的价值不大。此外，现有的工作利用纹理方面和图像数量来预测答案质量，但很少考虑答案图像固有的语义信息。为了弥补研究差距，我们设计了一个工件：答案顾问 (AA)，以帮助回答者给出广受好评的答案。我们的 AA 使用图像文本一致性度量，该度量是通过将主题建模与深度学习方法相结合而获得的。在真实数据集上，所提出的措施可以将答案实际发布到问答网站之前答案流行度的预测误差降低 38.12%。