  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.
