Dialog state tracking, a machine reading approach using Memory Network
Table 3: One supporting fact task : Acc. obtained on DSTC2 test set
['Model', 'Area', 'Food', 'Price', 'Joint']
We compare with two established utterance-level discriminative neural trackers, a Recurrent Neural Network (RNN) model [Henderson2014d] and the Neural Belief Tracker [SWTY16]. As suggested in this last work, the first RNN baseline model uses no semantic (i.e. synonym) dictionary, while the improved baseline uses a hand-crafted semantic dictionary designed for the DSTC2 ontology. In this context, a MemN2N model allows to obtain competitive results with the most close, non-memory enhanced, state of the art approach of recurrent neural network with word embedding as prior knowledge.