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\n  \n 2023\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n Rethinking Representations: A Log-bilinear Model of Phonotactics.\n \n \n \n \n\n\n \n Dai, H.; Mayer, C.; and Futrell, R.\n\n\n \n\n\n\n In Proceedings of the Society for Computation in Linguistics, volume 6, 2023. \n \n\n\n\n
\n\n\n\n \n \n \"RethinkingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{dai_rethinking_2023,\n\ttitle = {Rethinking {Representations}: {A} {Log}-bilinear {Model} of {Phonotactics}},\n\tvolume = {6},\n\tshorttitle = {Rethinking {Representations}},\n\turl = {https://scholarworks.umass.edu/scil/vol6/iss1/24/},\n\tdoi = {10.7275/EBV1-5G73},\n\tabstract = {Models of phonotactics include subsegmental representations in order to generalize to\nunattested sequences. These representations\ncan be encoded in at least two ways: as discrete, phonetically-based features, or as continuous, distribution-based representations induced from the statistical patterning of sounds.\nBecause phonological theory typically assumes\nthat representations are discrete, past work has\nreduced continuous representations to discrete\nones, which eliminates potentially relevant information. In this paper we present a model\nof phonotactics that can use continuous representations directly, and show that this approach\nyields competitive performance on modeling\nexperimental judgments of English sonority sequencing. The proposed model broadens the\nspace of possible phonotactic models by removing requirements for discrete features, and is\na step towards an integrated picture of phonotactic learning based on distributional statistics\nand continuous representations.},\n\turldate = {2023-08-05},\n\tbooktitle = {Proceedings of the {Society} for {Computation} in {Linguistics}},\n\tauthor = {Dai, Huteng and Mayer, Connor and Futrell, Richard},\n\tyear = {2023},\n}\n\n
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\n Models of phonotactics include subsegmental representations in order to generalize to unattested sequences. These representations can be encoded in at least two ways: as discrete, phonetically-based features, or as continuous, distribution-based representations induced from the statistical patterning of sounds. Because phonological theory typically assumes that representations are discrete, past work has reduced continuous representations to discrete ones, which eliminates potentially relevant information. In this paper we present a model of phonotactics that can use continuous representations directly, and show that this approach yields competitive performance on modeling experimental judgments of English sonority sequencing. The proposed model broadens the space of possible phonotactic models by removing requirements for discrete features, and is a step towards an integrated picture of phonotactic learning based on distributional statistics and continuous representations.\n
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\n  \n 2022\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n Learning Phonotactics in a Differentiable Framework of Subregular Languages.\n \n \n \n \n\n\n \n Dai, H.; and Futrell, R.\n\n\n \n\n\n\n In Proceedings of the Annual Meetings on Phonology, volume 9, 2022. \n \n\n\n\n
\n\n\n\n \n \n \"LearningPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{dai_learning_2022,\n\ttitle = {Learning {Phonotactics} in a {Differentiable} {Framework} of {Subregular} {Languages}},\n\tvolume = {9},\n\turl = {https://journals.linguisticsociety.org/proceedings/index.php/amphonology/article/view/5173/4840},\n\tabstract = {Phonotactic constraints have been argued to beregular, meaning that they can be represented usingfinite-state automata (Heinz, 2018); furthermore, they have been argued to occupy a even more restrictedregion of the regular language class known as the subregular hierarchy (Rogers \\& Pullum, 2011). Ourcontribution is to present a simple model of phonotactic learning from positive evidence. Our approach isbased on probabilistic finite-state automata (Vidal et al., 2005a, b). We study the model’s ability to induce localand nonlocal phonotactics from wordlist data, both with and without formal constraints on the automaton. In particular, we evaluate the ability of our learner to induce nonlocal phonotactic constraints from data ofNavajo and Quechua. Our work provides a framework in which different formal models of phonotactics canbe compared, and sheds light on the structural nature of phonological acquisition (Dai, 2021; Shibata \\& Heinz, 2019; Heinz \\& Rogers, 2010, 2013).},\n\tbooktitle = {Proceedings of the {Annual} {Meetings} on {Phonology}},\n\tauthor = {Dai, Huteng and Futrell, Richard},\n\tyear = {2022},\n}\n\n
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\n Phonotactic constraints have been argued to beregular, meaning that they can be represented usingfinite-state automata (Heinz, 2018); furthermore, they have been argued to occupy a even more restrictedregion of the regular language class known as the subregular hierarchy (Rogers & Pullum, 2011). Ourcontribution is to present a simple model of phonotactic learning from positive evidence. Our approach isbased on probabilistic finite-state automata (Vidal et al., 2005a, b). We study the model’s ability to induce localand nonlocal phonotactics from wordlist data, both with and without formal constraints on the automaton. In particular, we evaluate the ability of our learner to induce nonlocal phonotactic constraints from data ofNavajo and Quechua. Our work provides a framework in which different formal models of phonotactics canbe compared, and sheds light on the structural nature of phonological acquisition (Dai, 2021; Shibata & Heinz, 2019; Heinz & Rogers, 2010, 2013).\n
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\n  \n 2021\n \n \n (4)\n \n \n
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\n \n\n \n \n \n \n \n \n Learning Nonlocal Phonotactics in a Strictly Piecewise Probabilistic Phonotactic Model.\n \n \n \n \n\n\n \n Dai, H.\n\n\n \n\n\n\n In Proceedings of the Annual Meetings on Phonology, volume 8, 2021. \n \n\n\n\n
\n\n\n\n \n \n \"LearningPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{dai_learning_2021,\n\ttitle = {Learning {Nonlocal} {Phonotactics} in a {Strictly} {Piecewise} {Probabilistic} {Phonotactic} {Model}},\n\tvolume = {8},\n\turl = {https://journals.linguisticsociety.org/proceedings/index.php/amphonology/article/view/4893},\n\tdoi = {https://doi.org/10.3765/amp.v9i0.4893},\n\tbooktitle = {Proceedings of the {Annual} {Meetings} on {Phonology}},\n\tauthor = {Dai, Huteng},\n\tyear = {2021},\n}\n\n
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\n \n\n \n \n \n \n \n \n Simple induction of (deterministic) probabilistic finite-state automata for phonotactics by stochastic gradient descent.\n \n \n \n \n\n\n \n Dai, H.; and Futrell, R.\n\n\n \n\n\n\n In Proceedings of the 18th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology, pages 167–176, August 2021. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"SimplePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{dai_simple_2021,\n\ttitle = {Simple induction of (deterministic) probabilistic finite-state automata for phonotactics by stochastic gradient descent},\n\turl = {https://aclanthology.org/2021.sigmorphon-1.19},\n\tdoi = {10.18653/v1/2021.sigmorphon-1.19},\n\tabstract = {We introduce a simple and highly general phonotactic learner which induces a probabilistic finite-state automaton from word-form data. We describe the learner and show how to parameterize it to induce unrestricted regular languages, as well as how to restrict it to certain subregular classes such as Strictly k-Local and Strictly k-Piecewise languages. We evaluate the learner on its ability to learn phonotactic constraints in toy examples and in datasets of Quechua and Navajo. We find that an unrestricted learner is the most accurate overall when modeling attested forms not seen in training; however, only the learner restricted to the Strictly Piecewise language class successfully captures certain nonlocal phonotactic constraints. Our learner serves as a baseline for more sophisticated methods.},\n\tbooktitle = {Proceedings of the 18th {SIGMORPHON} {Workshop} on {Computational} {Research} in {Phonetics}, {Phonology}, and {Morphology}},\n\tpublisher = {Association for Computational Linguistics},\n\tauthor = {Dai, Huteng and Futrell, Richard},\n\tmonth = aug,\n\tyear = {2021},\n\tpages = {167--176},\n}\n\n
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\n We introduce a simple and highly general phonotactic learner which induces a probabilistic finite-state automaton from word-form data. We describe the learner and show how to parameterize it to induce unrestricted regular languages, as well as how to restrict it to certain subregular classes such as Strictly k-Local and Strictly k-Piecewise languages. We evaluate the learner on its ability to learn phonotactic constraints in toy examples and in datasets of Quechua and Navajo. We find that an unrestricted learner is the most accurate overall when modeling attested forms not seen in training; however, only the learner restricted to the Strictly Piecewise language class successfully captures certain nonlocal phonotactic constraints. Our learner serves as a baseline for more sophisticated methods.\n
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\n \n\n \n \n \n \n \n \n Learning Underlying Representations and Input-Strictly-Local Functions.\n \n \n \n \n\n\n \n Hua, W.; Jardine, A.; and Dai, H.\n\n\n \n\n\n\n In Proceedings of the 37th West Coast Conference on Formal Linguistics, 2021. Cascadilla Press\n \n\n\n\n
\n\n\n\n \n \n \"LearningPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{hua_learning_2021,\n\ttitle = {Learning {Underlying} {Representations} and {Input}-{Strictly}-{Local} {Functions}},\n\turl = {https://hutengdai.com/files/huaetalWCCFL2020ms.pdf},\n\tbooktitle = {Proceedings of the 37th {West} {Coast} {Conference} on {Formal} {Linguistics}},\n\tpublisher = {Cascadilla Press},\n\tauthor = {Hua, Wenyue and Jardine, Adam and Dai, Huteng},\n\tyear = {2021},\n}\n\n
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\n \n\n \n \n \n \n \n \n Gradient similarity in Lezgian laryngeal harmony: representation and computation.\n \n \n \n \n\n\n \n Dai, H.\n\n\n \n\n\n\n In Proceedings of the 38th West Coast Conference on Formal Linguistics, 2021. Cascadilla Press\n \n\n\n\n
\n\n\n\n \n \n \"GradientPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{dai_gradient_2021,\n\ttitle = {Gradient similarity in {Lezgian} laryngeal harmony: representation and computation},\n\turl = {https://hutengdai.com/files/Lezgian_paper_WCCFL.pdf},\n\tbooktitle = {Proceedings of the 38th {West} {Coast} {Conference} on {Formal} {Linguistics}},\n\tpublisher = {Cascadilla Press},\n\tauthor = {Dai, Huteng},\n\tyear = {2021},\n}\n\n
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\n  \n 2020\n \n \n (2)\n \n \n
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\n \n\n \n \n \n \n \n \n Global and local pitch level in Caabe is not predicted by the frequency code.\n \n \n \n \n\n\n \n Mamadou Yacoubou, T.; D'Imperio, M.; Dai, H.; and Kleinschmidt, D.\n\n\n \n\n\n\n The Journal of the Acoustical Society of America, 148(4): 2725–2725. October 2020.\n \n\n\n\n
\n\n\n\n \n \n \"GlobalPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{mamadou_yacoubou_global_2020,\n\ttitle = {Global and local pitch level in {Caabe} is not predicted by the frequency code},\n\tvolume = {148},\n\tissn = {0001-4966},\n\turl = {http://asa.scitation.org/doi/10.1121/1.5147569},\n\tdoi = {10.1121/1.5147569},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-11-08},\n\tjournal = {The Journal of the Acoustical Society of America},\n\tauthor = {Mamadou Yacoubou, Tajudeen and D'Imperio, Mariapaola and Dai, Huteng and Kleinschmidt, David},\n\tmonth = oct,\n\tyear = {2020},\n\tpages = {2725--2725},\n}\n\n
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\n \n\n \n \n \n \n \n \n Information-theoretic Characterization of the Sub-regular Hierarchy.\n \n \n \n \n\n\n \n Dai, H.; and Futrell, R.\n\n\n \n\n\n\n In Proceedings of the Society for Computation in Linguistics, volume 3, pages 445–448, January 2020. \n \n\n\n\n
\n\n\n\n \n \n \"Information-theoreticPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
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@inproceedings{dai_information-theoretic_2020,\n\ttitle = {Information-theoretic {Characterization} of the {Sub}-regular {Hierarchy}},\n\tvolume = {3},\n\turl = {https://scholarworks.umass.edu/scil/vol3/iss1/46},\n\tdoi = {https://doi.org/10.7275/c521-qn83},\n\tbooktitle = {Proceedings of the {Society} for {Computation} in {Linguistics}},\n\tauthor = {Dai, Huteng and Futrell, Richard},\n\tmonth = jan,\n\tyear = {2020},\n\tkeywords = {Information Theory},\n\tpages = {445--448},\n}\n
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\n  \n 2019\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n Nasal Sound Changes in Naish Languages.\n \n \n \n\n\n \n Li, Z.; Pan, J.; and Dai, H.\n\n\n \n\n\n\n Bulletin of Linguistic Studies, 23. 2019.\n \n\n\n\n
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@article{li_nasal_2019,\n\ttitle = {Nasal {Sound} {Changes} in {Naish} {Languages}},\n\tvolume = {23},\n\tcopyright = {All rights reserved},\n\tjournal = {Bulletin of Linguistic Studies},\n\tauthor = {Li, Zihe and Pan, Jingjing and Dai, Huteng},\n\tyear = {2019},\n}\n\n
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