Community question answering (CQA) forums are Internet-based platforms where
users ask questions about a topic and other expert users try to provide
solutions. Many CQA forums such as Quora, Stackoverflow, Yahoo!Answer,
StackExchange exist with a lot of user-generated data. These data are leveraged
in automated CQA ranking systems where similar questions (and answers) are
presented in response to the query of the user. In this work, we empirically
investigate a few aspects of this domain. Firstly, in addition to traditional
features like TF-IDF, BM25 etc., we introduce a BERT-based feature that
captures the semantic similarity between the question and answer. Secondly,
most of the existing research works have focused on features extracted only
from the question part; features extracted from answers have not been explored
extensively. We combine both types of features in a linear fashion. Thirdly,
using our proposed concepts, we conduct an empirical investigation with
different rank-learning algorithms, some of which have not been used so far in
CQA domain. On three standard CQA datasets, our proposed framework achieves
state-of-the-art performance. We also analyze importance of the features we use
in our investigation. This work is expected to guide the practitioners to
select a better set of features for the CQA retrieval task.

在这项工作中，我们通过实证研究了社区问答论坛的几个方面，包括引入一种基于 BERT 的特征捕捉问题和答案的语义相似性、使用线性方式结合问题和答案特征、以及在 CQA 领域中采用迄今为止未使用过的排序学习算法等。在三个标准 CQA 数据集上，我们的提出的框架实现了最领先的性能，并分析了我们使用的特征的重要性。这项工作有望指导实践者在 CQA 检索任务中选择更好的特征集。