Experiments on two domains of the MultiDoGO dataset reveal challenges of constraint violation detection and sets the stage for future work and improvements. The outcomes from the empirical work present that the new ranking mechanism proposed can be more effective than the previous one in a number of points. Extensive experiments and analyses on the lightweight models present that our proposed strategies achieve considerably greater scores and substantially 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 author Tobias Falke creator Caglar Tirkaz author Daniil Sorokin creator 2020-dec text Proceedings of the 28th International Conference on Computational Linguistics: Industry Track International Committee on Computational Linguistics Online conference publication Recent progress through advanced neural models pushed the efficiency of process-oriented dialog programs to almost good accuracy on present benchmark datasets for intent classification and slot labeling.