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• W3128459263 abstract "Uncertainty estimation is important for ensuring safety and robustness of AI systems. While most research in the area has focused on un-structured prediction tasks, limited work has investigated general uncertainty estimation approaches for structured prediction. Thus, this work aims to investigate uncertainty estimation for autoregressive structured prediction tasks within a single unified and interpretable probabilistic ensemble-based framework. We consider: uncertainty estimation for sequence data at the token-level and complete sequence-level; interpretations for, and applications of, various measures of uncertainty; and discuss both the theoretical and practical challenges associated with obtaining them. This work also provides baselines for token-level and sequence-level error detection, and sequence-level out-of-domain input detection on the WMT'14 English-French and WMT'17 English-German translation and LibriSpeech speech recognition datasets." @default.
• W3128459263 created "2021-02-15" @default.
• W3128459263 creator A5050766679 @default.
• W3128459263 creator A5080001860 @default.
• W3128459263 date "2020-02-18" @default.
• W3128459263 modified "2023-09-27" @default.
• W3128459263 title "Uncertainty Estimation in Autoregressive Structured Prediction" @default.
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• W3128459263 hasPublicationYear "2020" @default.
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