Multi-Granularity Representations of Dialog
Table 3: Results for next utterance retrieval on the Ubuntu dialog corpus. This table shows previous work, and experimental results with two underlying architectures: a dual encoder model and Deep Attention Matching networks. The results shown in the DAM experiments section are performed with the open-sourced implementation of Zhou et al. (2018), which obtains slightly worse performance than they report. All bold-face results are statistically significant to p<0.01.
['[BOLD] Model Name', '[BOLD] MRR', '[BOLD] R10@1', '[BOLD] R2@1']
Across both base architectures, MGT outperforms ensembling. The primary difference between these two methods is that MGT explicitly ensures that several granularities of representation are learned. As such, these results reaffirm the hypothesis that learning multiple granularities of representation leads to more diverse models, and more general representations of dialog.