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SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection ...
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Information-Theoretic Probing for Linguistic Structure ...
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Information-Theoretic Probing for Linguistic Structure ...
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Predicting Declension Class from Form and Meaning ...
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Predicting declension class from form and meaning
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6
Pareto Probing: Trading Off Accuracy for Complexity
In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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Predicting Declension Class from Form and Meaning
In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)
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A Tale of a Probe and a Parser
In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)
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Information-Theoretic Probing for Linguistic Structure
In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)
Abstract: The success of neural networks on a diverse set of NLP tasks has led researchers to question how much these networks actually "know" about natural language. Probes are a natural way of assessing this. When probing, a researcher chooses a linguistic task and trains a supervised model to predict annotations in that linguistic task from the network's learned representations. If the probe does well, the researcher may conclude that the representations encode knowledge related to the task. A commonly held belief is that using simpler models as probes is better; the logic is that simpler models will identify linguistic structure, but not learn the task itself. We propose an information-theoretic operationalization of probing as estimating mutual information that contradicts this received wisdom: one should always select the highest performing probe one can, even if it is more complex, since it will result in a tighter estimate, and thus reveal more of the linguistic information inherent in the representation. The experimental portion of our paper focuses on empirically estimating the mutual information between a linguistic property and BERT, comparing these estimates to several baselines. We evaluate on a set of ten typologically diverse languages often underrepresented in NLP research-plus English-totalling eleven languages. Our implementation is available in https://github.com/rycolab/info-theoretic-probing.
URL: https://hdl.handle.net/20.500.11850/446005
https://doi.org/10.3929/ethz-b-000446005
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10
Pareto Probing: Trading Off Accuracy for Complexity ...
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A Tale of a Probe and a Parser ...
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12
A Tale of a Probe and a Parser ...
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13
Predicting Declension Class from Form and Meaning ...
Williams, Adina; Pimentel, Tiago; Blix, Hagen. - : ETH Zurich, 2020
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