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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; Hao, Yiding; Mendelsohn, Simon; Frank, Robert. - : arXiv, 2019 Abstract: Neural network architectures have been augmented with differentiable stacks in order to introduce a bias toward learning hierarchy-sensitive regularities. It has, however, proven difficult to assess the degree to which such a bias is effective, as the operation of the differentiable stack is not always interpretable. In this paper, we attempt to detect the presence of latent representations of hierarchical structure through an exploration of the unsupervised learning of constituency structure. Using a technique due to Shen et al. (2018a,b), we extract syntactic trees from the pushing behavior of stack RNNs trained on language modeling and classification objectives. We find that our models produce parses that reflect natural language syntactic constituencies, demonstrating that stack RNNs do indeed infer linguistically relevant hierarchical structure. ... : To appear in the Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG; Neural and Evolutionary Computing cs.NE URL: https://dx.doi.org/10.48550/arxiv.1906.01594 https://arxiv.org/abs/1906.01594 BASE Hide details 10 Learnability and Overgeneration in Computational Syntax Hao, Yiding In: Proceedings of the Society for Computation in Linguistics (2019) BASE Show details

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