Integrating Question Classification and Deep Learning for improved Answer Selection
Integrating Question Classification and Deep Learning for improved Answer Selection
复制标题
集成问题分类和深度学习以改进答案选择
DOI：
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发表时间：
2018
期刊：
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通讯作者：
J. Barnden
中科院分区：
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作者：
Harish Tayyar Madabushi;Mark G. Lee;J. Barnden
我们提出了一个答案选择系统，它将细粒度问题分类与专为答案选择而设计的深度学习模型集成在一起。我们详细介绍了问题分类分类法和系统的必要更改、新实体识别系统的创建以及突出显示实体的方法以实现此目标。我们的实验表明，问题类对于深度学习模型的答案选择来说是一个强烈的信号，并且使我们能够在除一个实验之外的所有实验变体中超越当前的技术水平。在最佳配置中，我们的 MRR 和 MAP 分数在两个版本的 TREC 答案选择测试集（该任务的标准数据集）上均优于当前最先进的技术 3 到 5 分。
We present a system for Answer Selection that integrates fine-grained Question Classification with a Deep Learning model designed for Answer Selection. We detail the necessary changes to the Question Classification taxonomy and system, the creation of a new Entity Identification system and methods of highlighting entities to achieve this objective. Our experiments show that Question Classes are a strong signal to Deep Learning models for Answer Selection, and enable us to outperform the current state of the art in all variations of our experiments except one. In the best configuration, our MRR and MAP scores outperform the current state of the art by between 3 and 5 points on both versions of the TREC Answer Selection test set, a standard dataset for this task.