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Proficiency and the Use of Machine Translation: A Case Study of Four Japanese Learners
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In: L2 Journal, vol 14, iss 1 (2022)
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On the characteristic of personal reference terms in Korean: A comparison with Japanese based on TV dramas
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In: Conference Proceedings for the 9th Korean Studies Association of Australia (KSAA) Biennial Conference 2015 (2022)
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A Proposed Resolution to the Problem of Geographical Inversion in Japanese Language Origins
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Το haiku, η δυτική του πρόσληψη και αναφορές στην αξιοποίησή του από τον Γιώργο Σεφέρη ...
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Prediction of upcoming pitch accent using Sandhi rules in Kansai Japanese: A web-based visual world eye-tracking study ...
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Lexical Category and Downstep in Japanese
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In: Languages; Volume 7; Issue 1; Pages: 25 (2022)
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Estimating the Emotional Information in Japanese Songs Using Search Engines
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In: Sensors; Volume 22; Issue 5; Pages: 1800 (2022)
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Effects of Japanese Special Moras Education Using Evernote
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In: Education Sciences; Volume 12; Issue 4; Pages: 270 (2022)
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On the Limits of Across-the-Board Movement: Distributed Extraction Coordinations
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In: Philosophies; Volume 7; Issue 1; Pages: 10 (2022)
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Predicate Morphology and Narrative Structure in Early Heian Japanese Buddhist Texts: A Case Study of the Saidaiji Golden Light Sutra
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Language learning through interaction: Online and in the classroom
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In: The Coastal Review: An Online Peer-reviewed Journal (2022)
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Translation and equivalences between languages and cultures: Portuguese and Japanese proverbs ; 言語と文化の中にある翻訳と互換性: ポルトガル語と日本語の格言から
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Teixeira, José. - : Aichi Prefectural University. Graduate School of International Cultural Studies, 2022
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Learning Argument Structures with Recurrent Neural Network Grammars
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In: Proceedings of the Society for Computation in Linguistics (2022)
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Abstract:
In targeted syntactic evaluations, the syntactic competence of LMs has been investigated through various syntactic phenomena, among which one of the important domains has been argument structure. Argument structures in head-initial languages have been exclusively tested in the previous literature, but may be readily predicted from lexical information of verbs, potentially overestimating the syntactic competence of LMs. In this paper, we explore whether argument structures can be learned by LMs in head-final languages, which could be more challenging given that argument structures must be predicted before encountering verbs during incremental sentence processing, so that the relative weight of syntactic information should be heavier than lexical information. Specifically, we examined double accusative constraint and double dative constraint in Japanese with the sequential and hierarchical LMs: n-gram model, LSTM, GPT-2, and RNNG. Our results demonstrated that the double accusative constraint is captured by all LMs, whereas the double dative constraint is successfully explained only by the hierarchical model. In addition, we probed incremental sentence processing by LMs through the lens of surprisal, and suggested that the hierarchical model may capture deep semantic roles that verbs assign to arguments, while the sequential models seem to be influenced by surface case alignments.
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Keyword:
acceptability; argument structure; Computational Linguistics; grammaticality; Japanese; language model; probability; structure
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URL: https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1258&context=scil https://scholarworks.umass.edu/scil/vol5/iss1/9
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