Product-specific community question answering platforms can greatly help
address the concerns of potential customers. However, the user-provided answers
on such platforms often vary a lot in their qualities. Helpfulness votes from
the community can indicate the overall quality of the answer, but they are
often missing. Accurately predicting the helpfulness of an answer to a given
question and thus identifying helpful answers is becoming a demanding need.
Since the helpfulness of an answer depends on multiple perspectives instead of
only topical relevance investigated in typical QA tasks, common answer
selection algorithms are insufficient for tackling this task. In this paper, we
propose the Review-guided Answer Helpfulness Prediction (RAHP) model that not
only considers the interactions between QA pairs but also investigates the
opinion coherence between the answer and crowds' opinions reflected in the
reviews, which is another important factor to identify helpful answers.
Moreover, we tackle the task of determining opinion coherence as a language
inference problem and explore the utilization of pre-training strategy to
transfer the textual inference knowledge obtained from a specifically designed
trained network. Extensive experiments conducted on real-world data across
seven product categories show that our proposed model achieves superior
performance on the prediction task.

本文提出了一种基于评论引导的答案有用性预测模型，该模型不仅考虑了 QA 对之间的交互，还探讨了答案与众筹意见之间的意见连贯性作为识别有用答案的另一个重要因素，并探索了利用预训练策略将从一个特定设计的经过训练的网络中获得的文本推理知识转移的方法。该模型在七个产品类别上进行的广泛实验表明，其在预测任务中的表现优异。