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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 11 of 11 1 Speakers Enhance Contextually Confusable Words Meinhardt, Eric; Bakovic, Eric; Bergen, Leon. - : eScholarship, University of California, 2020 BASE Show details 2 Predicting Declension Class from Form and Meaning Cotterell, Ryan; Blix, Hagen; Pimentel, Tiago... In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020) BASE Show details 3 The Paradigm Discovery Problem Wu, Shijie; Habash, Nizar; Elsner, Micha... In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020) BASE Show details 4 A Tale of a Probe and a Parser Valvoda, Josef; Hall Maudslay, Rowan; Williams, Adina... In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020) BASE Show details 5 A Corpus for Large-Scale Phonetic Typology Chodroff, Eleanor; Cotterell, Ryan; Wiesner, Matthew... In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020) BASE Show details 6 Information-Theoretic Probing for Linguistic Structure Pimentel, Tiago; Zmigrod, Ran; Cotterell, Ryan; Williams, Adina; Maudslay, Rowan H.; Valvoda, Josef 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 BASE Hide details 7 It’s Easier to Translate out of English than into it: Measuring Neural Translation Difficulty by Cross-Mutual Information Mielke, Sabrina J.; Okazaki, Naoaki; Anastasopoulos, Antonios... In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020) BASE Show details 8 ASSET: A dataset for tuning and evaluation of sentence simplification models with multiple rewriting transformations Alva Manchego, Fernando; Martin, Louis; Bordes, Antoine. - : ACL, 2020 BASE Show details 9 Non-linear instance-based cross-lingual mapping for non-isomorphic embedding spaces Glavaš, Goran; Vulić, Ivan. - : Association for Computational Linguistics, 2020 BASE Show details 10 Classification-based self-learning for weakly supervised bilingual lexicon induction Vulić, Ivan; Korhonen, Anna; Glavaš, Goran. - : Association for Computational Linguistics, 2020 BASE Show details 11 On the limitations of cross-lingual encoders as exposed by reference-free machine translation evaluation Zhao, Wei; Glavaš, Goran; Peyrard, Maxime. - : Association for Computational Linguistics, 2020 BASE Show details

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