论文标题
基于模板的问题回答使用递归神经网络
Template-based Question Answering using Recursive Neural Networks
论文作者
论文摘要
我们提出了一种基于神经网络的方法，可以使用递归神经网络自动学习和将自然语言问题分类为相应的模板。使用神经网络的一个明显优势是消除了对可能繁琐且容易出错的费力工程的需求。输入问题被编码为向量表示。该模型在LC-Quad数据集（大规模复杂问题答案数据集）上进行了训练和评估。 LC-Quad查询是根据模型试图分类的38个唯一模板进行注释的。根据LC-Quad数据集和第7个问题回答有关链接数据（QALD-7）数据集的第7个问题。递归神经网络可在LC-Quad数据集上达到0.828的模板分类精度，而QALD-7数据集上的精度为0.618。当Top-2最有可能被视为模板时，该模型在LC-Quad数据集上的精度为0.945，而QALD-7数据集则达到了0.786。插槽填充后，总体系统在LC-Quad数据集上达到了0.419的宏F-评分，而QALD-7数据集的宏F-SCORE为0.417。
We propose a neural network-based approach to automatically learn and classify natural language questions into its corresponding template using recursive neural networks. An obvious advantage of using neural networks is the elimination of the need for laborious feature engineering that can be cumbersome and error-prone. The input question is encoded into a vector representation. The model is trained and evaluated on the LC-QuAD dataset (Large-scale Complex Question Answering Dataset). The LC-QuAD queries are annotated based on 38 unique templates that the model attempts to classify. The resulting model is evaluated against both the LC-QuAD dataset and the 7th Question Answering Over Linked Data (QALD-7) dataset. The recursive neural network achieves template classification accuracy of 0.828 on the LC-QuAD dataset and an accuracy of 0.618 on the QALD-7 dataset. When the top-2 most likely templates were considered the model achieves an accuracy of 0.945 on the LC-QuAD dataset and 0.786 on the QALD-7 dataset. After slot filling, the overall system achieves a macro F-score 0.419 on the LC-QuAD dataset and a macro F-score of 0.417 on the QALD-7 dataset.
