An Analysis of Dialogue Act Sequence Similarity Across Multiple Domains (2022.lrec-1)
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| Challenge: | a recent study shows that many machine learning models perform poorly when exposed to domain shifts due to contextual differences. |
| Approach: | They analyze dialogue act sequences from related domains to predict performance degradation . they find that when dialogue acts sequences are dissimilar they lie further away in embedding space . |
| Outcome: | The proposed model can be trained even when the datasets are corrupted with noise. |
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Mihail Eric, Rahul Goel, Shachi Paul, Abhishek Sethi, Sanchit Agarwal, Shuyang Gao, Adarsh Kumar, Anuj Goyal, Peter Ku, Dilek Hakkani-Tur
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| Challenge: | Recent work in Dialogue Act classification has treated the task as a sequence labeling problem using hierarchical deep neural networks. |
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| Challenge: | Existing approaches to open-domain dialogue generation ignore the nature of 1-to-1 mapping that there may exist multiple valid responses corresponding to the same query. |
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| Challenge: | Existing models for machine translation and dialogue response generation require a large number of handcrafted features. |
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| Challenge: | Existing pipeline approaches for task-oriented dialogue systems tend to predict multiple dialogue acts first and use them to assist response generation. |
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| Challenge: | Existing benchmark datasets for open-domain dialogue generation are advancing the field . overlapping between training and test sets can cause fake performance . |
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Augmenting Small Data to Classify Contextualized Dialogue Acts for Exploratory Visualization (2020.lrec-1)
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| Challenge: | In the last decade, natural language processing and machine learning have come a long way towards building an automated dialogue system. |
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SuperDialseg: A Large-scale Dataset for Supervised Dialogue Segmentation (2023.emnlp-main)
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| Challenge: | Empirical studies show that supervised learning is extremely effective in in-domain datasets and models trained on SuperDialseg can achieve good generalization ability on out-of-domain data. |
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