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SE367 Course Project Shourya Sonkar Roy Burman (Y8487) Learning Grammatical Gender in an Artificial Language Based on Hindi
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Introduction Is grammatical gender an arbitrarily defined categorisation? Position: No, grammatical gender is acquired by an individual based on two cues: Distributional Cues: Co-occurrence with gender marked articles, verbs, adjective etc. in a certain manner Phonological Cues: Similar sounding words are acquired as same gender Objective of the study is to support this position using an artificial language constructed using Hindi syllables and Hindi sentence structures
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Experiment Design
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The Artificial Language Design similar to that of Mirkovic J., Forrest S. & Gaskell M. G. (2011) Based on Hindi pronounceable syllables Masculine verbs: पातु and जीमु Feminine Verbs: वज and फोस Masculine Nouns: Words with 2-3 syllables, which have no meaning in Hindi, as checked in Google Translate. Feminine Nouns: Words with 2-3 syllables ending with ू or ो, which have no meaning in Hindi, as checked in Google Translate. Mirkovic J., Forrest S. & Gaskell M. G. (2011). Semantic Regularities in Grammatical Categories: Learning Grammatical Gender in an Artificial Language. Proceedings of the 33rd Annual Conference of the Cognitive Science Society
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The Artificial Language Sentence constructions are [Noun] [verb] है ! कबू वज है ! The sentences were then recorded in the voice of a UP resident (Bimodal learning) Modifications to the experiment suggested by Prof. Achla Raina, Department of Humanities and Social Sciences
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Schedule of Tasks DayTask(s) 1Word-picture Matching 2 Verb selection Word-picture Matching 3 Word-picture Matching (on Generalisation set) Verb selection Participants: Five employees in the Hall 1 Mess
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Examples of Tasks Size of word-picture matching dataset: 44 Size of the generalisation set: 8
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पातु / जीमु वज / फोस
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Results
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Word-Picture Matching (Day 1)
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Discussion The participants are learning the artificial nouns. The learning rates differed between individuals.
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Thank you!
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