Experiments on two domains of the MultiDoGO dataset reveal challenges of constraint violation detection and units the stage for future work and improvements. The results from the empirical work present that the new rating mechanism proposed can be more practical than the previous one in several facets. Extensive experiments and analyses on the lightweight fashions present that our proposed methods obtain considerably increased scores and considerably improve the robustness of both intent detection and slot filling. Data-Efficient Paraphrase Generation to Bootstrap Intent Classification and Slot Labeling for new Features in Task-Oriented Dialog Systems Shailza Jolly writer Tobias Falke author Caglar Tirkaz author Daniil Sorokin creator 2020-dec textual content Proceedings of the twenty eighth International Conference on Computational Linguistics: Industry Track International Committee on Computational Linguistics Online conference publication Recent progress through advanced neural models pushed the performance of activity-oriented dialog systems to almost excellent accuracy on existing benchmark datasets for intent classification and slot labeling.