Experiments on two domains of the MultiDoGO dataset reveal challenges of constraint violation detection and sets the stage for future work and enhancements. The results from the empirical work present that the brand new rating mechanism proposed shall be more effective than the former one in several elements. Extensive experiments and analyses on the lightweight models show that our proposed strategies achieve considerably greater 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 brand new Features in Task-Oriented Dialog Systems Shailza Jolly creator Tobias Falke creator Caglar Tirkaz creator Daniil Sorokin creator 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 superior neural models pushed the efficiency of process-oriented dialog programs to nearly good accuracy on existing benchmark datasets for intent classification and slot labeling.
