Given a question and its answer candidates (named QA corpus), answer selection is the task of identifying the most relevant answers to the question. Answer selection is widely used in question answering, web search, and so on. Current deep neural network models primarily utilize local features extracted from input question‐answer pairs (QA pairs). However, the global features contained in QA corpora are under‐utilized, and we argue that these global features substantially contribute to the answer selection task. To verify this point of view, we propose a novel model that combines local and global features for answer selection. In our model, two different global feature extractors are employed to extract statistical global features and deep global features from a QA corpus, respectively. Furthermore, we investigate the integration of these global features with local features in various experimental settings: statistical global features, deep global features, and a combination of statistical and deep global features. Our experimental results show that the global features are effective for answer selection. Our model obtains new state‐of‐the‐art results on two public answer selection datasets and performs especially well on YahooCQA, where it achieves 9.2 and 6% higher precision@1 (P@1) and mean reciprocal rank (MRR) scores than previously published models.

中文翻译：

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通过全局功能改善答案选择

给定一个问题及其答案候选者（称为QA语料库），选择答案是确定与该问题最相关的答案的任务。答案选择广泛用于问题解答，网络搜索等。当前的深度神经网络模型主要利用从输入问答对（QA对）中提取的局部特征。但是，QA语料库中包含的全局功能未得到充分利用，我们认为这些全局功能在很大程度上有助于答案选择任务。为了验证这种观点，我们提出了一种新颖的模型，该模型结合了局部和全局特征以进行答案选择。在我们的模型中，使用了两个不同的全局特征提取器分别从QA语料库中提取统计全局特征和深层全局特征。此外，我们研究了在各种实验环境中这些全局特征与局部特征的集成：统计全局特征，深度全局特征以及统计和深度全局特征的组合。我们的实验结果表明，全局特征对于答案选择是有效的。我们的模型在两个公共答案选择数据集上获得了最新的最新结果，并且在YahooCQA上表现特别出色，在此模型下，其@ 1（P @ 1）和平均平均排名（MRR）得分分别比9.2和6％高先前发布的模型。