  We show that a character-level encoder-decoder framework can be successfully
applied to question answering with a structured knowledge base. We use our
model for single-relation question answering and demonstrate the effectiveness
of our approach on the SimpleQuestions dataset (Bordes et al., 2015), where we
improve state-of-the-art accuracy from 63.9% to 70.9%, without use of
ensembles. Importantly, our character-level model has 16x fewer parameters than
an equivalent word-level model, can be learned with significantly less data
compared to previous work, which relies on data augmentation, and is robust to
new entities in testing.
