SCALABLE MULTI-DOMAIN DIALOGUE STATE TRACKING
Table 2: Joint goal accuracy results on different datasets to show the effect of slot level parameter sharing and comparison of Convolutional vs GRU based feature extraction.
['[BOLD] Dataset', '[BOLD] Feature Extraction', '[BOLD] Not Shared', '[BOLD] Shared']
We present two groups of experimental results. The first set of experiments compare the impact of model architectures and weight sharing on the three datasets. The two results columns in this table show results with no weight sharing between slots (i.e., weights are trained per slot type) and with weight sharing across all slots in each domain. For each dataset, results with CNN and GRU for representing system and user utterances is also presented. For both DSTC2 and simulated restaurant domain conversations, the GRU model with weight sharing resulted in better performance. The results are mixed with the movie domain conversations, but the movie dataset is also much smaller than the other two. In the rest of the experiments, we present results with weight sharing and GRUs.