We show that a character-level encoderdecoder framework can be successfully applied to question answering with a structured knowledge base. We use our model for singlerelation question answering and demonstrate the effectiveness of our approach on the Sim-pleQuestions 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. 1
REF propose a character-level encoder-decoder framework enhanced by the attention mechanism for singlerelation question answering.
Character-Level Question Answering with Attention
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing
Computer Science