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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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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 ... : Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics ...
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URL: http://hdl.handle.net/20.500.11850/446005
https://dx.doi.org/10.3929/ethz-b-000446005
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Please Mind the Root: Decoding Arborescences for Dependency Parsing
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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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Information-Theoretic Probing for Linguistic Structure
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In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)
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