Experiments on two domains of the MultiDoGO dataset reveal challenges of constraint violation detection and sets the stage for future work and improvements. The results from the empirical work present that the new ranking mechanism proposed shall be more practical than the former one in several points. Extensive experiments and analyses on the lightweight models present that our proposed strategies achieve considerably larger scores and substantially enhance 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 writer 2020-dec textual content Proceedings of the 28th International Conference on Computational Linguistics: Industry Track International Committee on Computational Linguistics Online convention publication Recent progress via advanced neural fashions pushed the efficiency of activity-oriented dialog methods to nearly perfect accuracy on current benchmark datasets for intent classification and slot labeling.
