  The task of answering natural language questions over knowledge bases has
received wide attention in recent years. Various deep learning architectures
have been proposed for this task. However, architectural design choices are
typically not systematically compared nor evaluated under the same conditions.
In this paper, we contribute to a better understanding of the impact of
architectural design choices by evaluating four different architectures under
the same conditions. We address the task of answering simple questions,
consisting in predicting the subject and predicate of a triple given a
question. In order to provide a fair comparison of different architectures, we
evaluate them under the same strategy for inferring the subject, and compare
different architectures for inferring the predicate. The architecture for
inferring the subject is based on a standard LSTM model trained to recognize
the span of the subject in the question and on a linking component that links
the subject span to an entity in the knowledge base. The architectures for
predicate inference are based on i) a standard softmax classifier ranging over
all predicates as output, iii) a model that predicts a low-dimensional encoding
of the property given entity representation and question, iii) a model that
learns to score a pair of subject and predicate given the question as well as
iv) a model based on the well-known FastText model. The comparison of
architectures shows that FastText provides better results than other
architectures.
