Multi-Domain Adversarial Learning for Slot Filling in Spoken Language Understanding
Table 2: Slot Filling F1 scores of the proposed method for data sets in different task domains.
['[BOLD] Model', '[BOLD] ATIS', '[BOLD] MIT  [BOLD] Rest.', '[BOLD] MIT Mov.  [BOLD] eng', '[BOLD] MIT Mov.  [BOLD] trivia10k13', '[BOLD] Comb.']
We compare the performance of domain-specific models, domain-general models, and joint domain-specific and domain-general models on data sets over multiple domains. For adversarial learning on domain-general models, we compare the model performance by applying different scales of domain adversarial losses. We further evaluate the model performance by jointly optimizing the domain-specific and domain-general model. The joint training model achieves larger gain on the MIT movie data set, improving the F1 score by 1.90% and 1.69% respectively on the eng corpus and the trivia10k13 corpus over their domain-specific models. Similar to the results in domain-general model evaluation, adversarial learning on domain-general model also brings benefit to the joint model optimization. Joint model training with adversarial learning achieves advanced slot filling performance comparing to non-adversarial joint model training on three out of four data sets.