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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 – 10 of 10 1 Formal Language Recognition by Hard Attention Transformers: Perspectives from Circuit Complexity ... Hao, Yiding; Angluin, Dana; Frank, Robert. - : arXiv, 2022 BASE Show details 2 An Adversarial Benchmark for Fake News Detection Models ... Flores, Lorenzo Jaime Yu; Hao, Yiding. - : arXiv, 2022 BASE Show details 3 Attribution Analysis of Grammatical Dependencies in LSTMs ... Hao, Yiding. - : arXiv, 2020 BASE Show details 4 Probabilistic Predictions of People Perusing: Evaluating Metrics of Language Model Performance for Psycholinguistic Modeling ... Hao, Yiding; Mendelsohn, Simon; Sterneck, Rachel. - : arXiv, 2020 BASE Show details 5 Rhythmic Syncope in Subregular Phonology Bowers, Dustin; Hao, Yiding In: University of Pennsylvania Working Papers in Linguistics (2020) BASE Show details 6 Metrical Grids and Generalized Tier Projection Hao, Yiding In: Proceedings of the Society for Computation in Linguistics (2020) BASE Show details 7 Computing Vowel Harmony: The Generative Capacity of Search & Copy Andersson, Samuel; Dolatian, Hossep; Hao, Yiding In: Proceedings of the Annual Meetings on Phonology; Proceedings of the 2019 Annual Meeting on Phonology ; 2377-3324 (2020) BASE Show details 8 Action-Sensitive Phonological Dependencies ... Hao, Yiding; Bowers, Dustin. - : arXiv, 2019 BASE Show details 9 Finding Syntactic Representations in Neural Stacks ... Merrill, William; Khazan, Lenny; Amsel, Noah. - : arXiv, 2019 BASE Show details 10 Learnability and Overgeneration in Computational Syntax Hao, Yiding In: Proceedings of the Society for Computation in Linguistics (2019) Abstract: This paper addresses the hypothesis that unnatural patterns generated by grammar formalisms can be eliminated on the grounds that they are unlearnable. I consider three examples of formal languages thought to represent dependencies unattested in natural language syntax, and show that all three can be learned by grammar induction algorithms following the Distributional Learning paradigm of Clark and Eyraud (2007). While learnable language classes are restrictive by necessity (Gold, 1967), these facts suggest that learnability alone may be insufficient for addressing concerns of overgeneration in syntax. Keyword: computational linguistics; context-free grammars; distributional learning; formal language theory; grammatical inference; learnability; machine learning; mathematical linguistics; minimalist grammars; multiple context-free grammars; overgeneration; poverty of the stimulus; syntax URL: https://scholarworks.umass.edu/scil/vol2/iss1/14 https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1092&context=scil BASE Hide details

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