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Experiments on two domains of the MultiDoGO dataset reveal challenges of constraint violation detection and sets the stage for future work and improvements. In experiments on a public dataset and with a real-world dialog system, we observe improvements for both intent classification and slot labeling, demonstrating the usefulness of our approach.
Unlike typical dialog models that rely on enormous, complex neural network architectures and huge-scale pre-skilled Transformers to achieve state-of-the-art results, our methodology achieves comparable results to BERT and even outperforms its smaller variant DistilBERT on conversational slot extraction duties.
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