se367 course project shourya sonkar roy burman (y8487) learning grammatical gender in an artificial...

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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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SE367 Course ProjectShourya Sonkar Roy Burman

(Y8487)

Learning Grammatical Gender in an Artificial Language Based

on Hindi

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

Experiment Design

The Artificial LanguageDesign similar to that of Mirkovic J., Forrest

S. & Gaskell M. G. (2011)Based on Hindi pronounceable syllablesMasculine verbs: पा�तु� and जी�मु� Feminine Verbs: वजी and फो�स 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

The Artificial Language

Sentence constructions are [Noun] [verb] है�!

कबू� वजी 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

Schedule of Tasks

Day Task(s)

1 Word-picture Matching

2Verb selection

Word-picture Matching

3

Word-picture MatchingWord-picture Matching (on

Generalisation set)Verb selection

Participants: Five employees in the Hall 1 Mess

Examples of Tasks

Size of word-picture matching dataset: 44Size of the generalisation set: 8

पा�तु�/जी�मु� वजी/फो�स

Results

Word-Picture Matching (Day 1)

Discussion

The participants are learning the artificial nouns.

The learning rates differed between individuals.

Thank you!