MALA: Cross-Domain Dialogue Generation with Action Learning
Table 2: Multi-Domain Joint Training Results
['Model', 'Model', 'SMD Entity-F1', 'SMD BLEU', 'MultiWOZ Entity-F1', 'MultiWOZ BLEU']
MALA improves dialogue task completion (measured by Entity-F1) while maintaining a high quality of language generation (measured by BLEU). For example, MALA-S3 (76.8) outperforms LaRL (71.3) by 7.71% under Entity-F1 on MultiWOZ, and has the highest BLEU score. Meanwhile, we also find that MALA benefits much from stage-I and stage-II in the joint learning setting. For example, MALA-S1 and MALA-S2 achieve 9.25% and 10.43% improvements over LIDM under Entity-F1 on SMD. This is largely because that, having complete dialogue state annotations, MALA can learn semantic latent actions in each domain at stage-I, and the action alignment at stage-II reduce action space for learning dialogue policy more effectively by finding shared actions across domains. We further find that LIDM and LaRL perform worse than Sequicity on SMD. The reason is that system utterances on SMD have shorter length and various expressions, making it challenging to capture underlying intentions merely based on surface realization. MALA overcomes this challenge by considering dialogue state transitions beyond surface realization in semantic latent action learning.