Goal-Embedded Dual Hierarchical Model for Task-Oriented Dialogue Generation
Table 1: Dialogue generation performance. All results are averaged over 5 random seeds and reported in percentage (%). Size: Total number of model parameters, P: Precision, R: Recall, L-D: Average dialogue length, L-U: Average utterance length.
['[BOLD] Model', '[BOLD] Size', '[BOLD] BLEU', '[BOLD] B1', '[BOLD] B2', '[BOLD] B3', '[BOLD] D-1', '[BOLD] D-2', '[BOLD] D-U', '[BOLD] P', '[BOLD] R', '[BOLD] F1', '[BOLD] L-D', '[BOLD] L-U']
For quality measures, G-DuHA significantly outperforms other baselines, implying that it’s able to carry out a higher-quality dialogue. Besides, goal-embedded LMs perform better than HREDs, showing the benefits of our goal embedding module. No significant performance difference is observed with respect to model size variants.