Multi-domain Dialogue State Tracking as Dynamic Knowledge Graph Enhanced Question Answering
Table 1: Results on MultiWOZ 2.0 dataset.
['[EMPTY]', 'Joint', 'Slot']
We compare with five published baselines. TRADE ( Wu et al., It utilizes an encoder-decoder architecture that takes dialogue contexts as source sentences, and takes state annotations as target sentences. to learn slot and utterance representations. Ablation Study: The first experiment completely removes the graph component, and the joint accuracy drops 0.47%. In the third experiment, we replace BQDi with the mean of query word embeddings and replace BCDj with the mean of context word embeddings. This is equivalent to setting the bi-directional attention scores uniformly. The joint accuracy significantly drops 1.62%. The fourth experiment completely removes the bi-directional attention layer, and the joint accuracy drops 1.85%. Both experiments show that bidirectional attention layer has a notably positive impact on model performance. The fifth experiment substitute ELMo embeddings with GloVe embeddings to demonstrate the benefit of using contextual word embeddings.