Modeling Human Morphological Competence

One of the central debates in the cognitive science of language has revolved around the nature of human linguistic competence. Whether syntactic competence should be characterized by abstract hierarchical structures or reduced to surface linear strings has been actively debated, but the nature of mo...

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Main Authors: Yohei Oseki, Alec Marantz
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-11-01
Series:Frontiers in Psychology
Subjects:
Online Access:https://www.frontiersin.org/articles/10.3389/fpsyg.2020.513740/full
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author Yohei Oseki
Yohei Oseki
Alec Marantz
Alec Marantz
Alec Marantz
author_facet Yohei Oseki
Yohei Oseki
Alec Marantz
Alec Marantz
Alec Marantz
author_sort Yohei Oseki
collection DOAJ
description One of the central debates in the cognitive science of language has revolved around the nature of human linguistic competence. Whether syntactic competence should be characterized by abstract hierarchical structures or reduced to surface linear strings has been actively debated, but the nature of morphological competence has been insufficiently appreciated despite the parallel question in the cognitive science literature. In this paper, in order to investigate whether morphological competence should be characterized by abstract hierarchical structures, we conducted a crowdsourced acceptability judgment experiment on morphologically complex words and evaluated five computational models of morphological competence against human acceptability judgments: Character Markov Models (Character), Syllable Markov Models (Syllable), Morpheme Markov Models (Morpheme), Hidden Markov Models (HMM), and Probabilistic Context-Free Grammars (PCFG). Our psycholinguistic experimentation and computational modeling demonstrated that “morphous” computational models with morpheme units outperformed “amorphous” computational models without morpheme units and, importantly, PCFG with hierarchical structures most accurately explained human acceptability judgments on several evaluation metrics, especially for morphologically complex words with nested morphological structures. Those results strongly suggest that human morphological competence should be characterized by abstract hierarchical structures internally generated by the grammar, not reduced to surface linear strings externally attested in large corpora.
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spelling doaj.art-e282194daa26479a821a626db275b3c42022-12-21T23:36:43ZengFrontiers Media S.A.Frontiers in Psychology1664-10782020-11-011110.3389/fpsyg.2020.513740513740Modeling Human Morphological CompetenceYohei Oseki0Yohei Oseki1Alec Marantz2Alec Marantz3Alec Marantz4Faculty of Science & Engineering, Waseda University, Tokyo, JapanDepartment of Linguistics, New York University, New York, NY, United StatesDepartment of Linguistics, New York University, New York, NY, United StatesDepartment of Psychology, New York University, New York, NY, United StatesNYU Abu Dhabi Institute, New York University, Abu Dhabi, United Arab EmiratesOne of the central debates in the cognitive science of language has revolved around the nature of human linguistic competence. Whether syntactic competence should be characterized by abstract hierarchical structures or reduced to surface linear strings has been actively debated, but the nature of morphological competence has been insufficiently appreciated despite the parallel question in the cognitive science literature. In this paper, in order to investigate whether morphological competence should be characterized by abstract hierarchical structures, we conducted a crowdsourced acceptability judgment experiment on morphologically complex words and evaluated five computational models of morphological competence against human acceptability judgments: Character Markov Models (Character), Syllable Markov Models (Syllable), Morpheme Markov Models (Morpheme), Hidden Markov Models (HMM), and Probabilistic Context-Free Grammars (PCFG). Our psycholinguistic experimentation and computational modeling demonstrated that “morphous” computational models with morpheme units outperformed “amorphous” computational models without morpheme units and, importantly, PCFG with hierarchical structures most accurately explained human acceptability judgments on several evaluation metrics, especially for morphologically complex words with nested morphological structures. Those results strongly suggest that human morphological competence should be characterized by abstract hierarchical structures internally generated by the grammar, not reduced to surface linear strings externally attested in large corpora.https://www.frontiersin.org/articles/10.3389/fpsyg.2020.513740/fullmorphologygrammaticalityacceptabilityprobabilitypsycholinguisticscomputational modeling
spellingShingle Yohei Oseki
Yohei Oseki
Alec Marantz
Alec Marantz
Alec Marantz
Modeling Human Morphological Competence
Frontiers in Psychology
morphology
grammaticality
acceptability
probability
psycholinguistics
computational modeling
title Modeling Human Morphological Competence
title_full Modeling Human Morphological Competence
title_fullStr Modeling Human Morphological Competence
title_full_unstemmed Modeling Human Morphological Competence
title_short Modeling Human Morphological Competence
title_sort modeling human morphological competence
topic morphology
grammaticality
acceptability
probability
psycholinguistics
computational modeling
url https://www.frontiersin.org/articles/10.3389/fpsyg.2020.513740/full
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