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Classification-based Quality Estimation: Small and Efficient Models for Real-world Applications ...
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A Generative Framework for Simultaneous Machine Translation ...
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Continual Quality Estimation with Online Bayesian Meta-Learning ...
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SentSim: Crosslingual Semantic Evaluation of Machine Translation ...
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What Makes a Scientific Paper be Accepted for Publication? ...
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MultiSubs: A Large-scale Multimodal and Multilingual Dataset ...
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Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP
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In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021)
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Classifying Dyads for Militarized Conflict Analysis
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In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021)
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Efficient Sampling of Dependency Structure
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In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021)
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Searching for More Efficient Dynamic Programs
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In: Findings of the Association for Computational Linguistics: EMNLP 2021 (2021)
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“Let Your Characters Tell Their Story”: A Dataset for Character-Centric Narrative Understanding
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In: Findings of the Association for Computational Linguistics: EMNLP 2021 (2021)
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A Bayesian Framework for Information-Theoretic Probing
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In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021)
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Abstract:
Pimentel et al. (2020) recently analysed probing from an information-theoretic perspective. They argue that probing should be seen as approximating a mutual information. This led to the rather unintuitive conclusion that representations encode exactly the same information about a target task as the original sentences. The mutual information, however, assumes the true probability distribution of a pair of random variables is known, leading to unintuitive results in settings where it is not. This paper proposes a new framework to measure what we term Bayesian mutual information, which analyses information from the perspective of Bayesian agents—allowing for more intuitive findings in scenarios with finite data. For instance, under Bayesian MI we have that data can add information, processing can help, and information can hurt, which makes it more intuitive for machine learning applications. Finally, we apply our framework to probing where we believe Bayesian mutual information naturally operationalises ease of extraction by explicitly limiting the available background knowledge to solve a task.
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URL: https://doi.org/10.3929/ethz-b-000518995 https://hdl.handle.net/20.500.11850/518995
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Findings of the WMT 2021 Shared Task on Quality Estimation ...
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Pushing the Right Buttons: Adversarial Evaluation of Quality Estimation ...
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The (Un)Suitability of Automatic Evaluation Metrics for Text Simplification ...
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