Template-based Question Answering using Recursive Neural Networks
This work addresses template-based question answering for knowledge graph queries, but it is incremental as it applies a known neural method to a specific dataset.
The paper tackled the problem of classifying natural language questions into templates using recursive neural networks, achieving a template classification accuracy of 0.828 on the LC-QuAD dataset and 0.618 on the QALD-7 dataset, with top-2 accuracies of 0.945 and 0.786, respectively.
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.