Community Question Answering (CQA) sites have spread and multiplied
significantly in recent years. Sites like Reddit, Quora, and Stack Exchange are
becoming popular amongst people interested in finding answers to diverse
questions. One practical way of finding such answers is automatically
predicting the best candidate given existing answers and comments. Many studies
were conducted on answer prediction in CQA but with limited focus on using the
background information of the questionnaires. We address this limitation using
a novel method for predicting the best answers using the questioner's
background information and other features, such as the textual content or the
relationships with other participants. Our answer classification model was
trained using the Stack Exchange dataset and validated using the Area Under the
Curve (AUC) metric. The experimental results show that the proposed method
complements previous methods by pointing out the importance of the
relationships between users, particularly throughout the level of involvement
in different communities on Stack Exchange. Furthermore, we point out that
there is little overlap between user-relation information and the information
represented by the shallow text features and the meta-features, such as time
differences.

本研究使用问题者的背景信息及其他特征，例如文本内容或者与其他参与者的关系，并利用 Stack Exchange 数据集进行训练，提出一种预测最佳答案的新方法，并且证明了用户间的关系对结果具有重要影响。同时，我们发现用户关系信息与文本特征和元特征（如时间差异）之间有较少的重叠。

利用用户信息在问答网站中预测最佳答案

Best-Answer Prediction in Q&A Sites Using User Information

In this paper, we investigate the problem of reasoning over natural language
statements. Prior neural based approaches do not explicitly consider the
inter-dependency among answers and their proofs. In this paper, we propose
PRobr, a novel approach for joint answer prediction and proof generation. PRobr
defines a joint probabilistic distribution over all possible proof graphs and
answers via an induced graphical model. We then optimize the model using
variational approximation on top of neural textual representation. Experiments
on multiple datasets under diverse settings (fully supervised, few-shot and
zero-shot evaluation) verify the effectiveness of PRobr, e.g., achieving
10%-30% improvement on QA accuracy in few/zero-shot evaluation. Our codes and
models can be found at this https URL

本文提出了一种新方法 PRobr，利用神经文本表示并定义一个概率分布来预测答案和生成证明，能在马尔可夫随机场中考虑答案之间的互相关联性， 实验表明该方法在几种数据集上实现了高准确率和提升。

自然证明生成的概率图推理

Probabilistic Graph Reasoning for Natural Proof Generation

Predicting the answer to a product-related question is an emerging field of
research that recently attracted a lot of attention. Answering subjective and
opinion-based questions is most challenging due to the dependency on
customer-generated content. Previous works mostly focused on review-aware
answer prediction; however, these approaches fail for new or unpopular
products, having no (or only a few) reviews at hand. In this work, we propose a
novel and complementary approach for predicting the answer for such questions,
based on the answers for similar questions asked on similar products. We
measure the contextual similarity between products based on the answers they
provide for the same question. A mixture-of-expert framework is used to predict
the answer by aggregating the answers from contextually similar products.
Empirical results demonstrate that our model outperforms strong baselines on
some segments of questions, namely those that have roughly ten or more similar
resolved questions in the corpus. We additionally publish two large-scale
datasets used in this work, one is of similar product question pairs, and the
second is of product question-answer pairs.

本论文提出了一种新型的预测产品相关问题答案的方法，该方法基于相似问题以及相似产品的答案来进行判断，并使用专家混合模型框架聚合答案来提高预测准确率。经实证结果表明，该模型在某些问题的表现优于强基线，而且我们还出版了两个大规模数据集用于支持我们的方法。