Codeswitched Sentence Creation using Dependency Parsing

TL;DR

This paper introduces a novel algorithm that uses dependency parsing to generate grammatically coherent code-switched sentences for English-Hindi, English-Marathi, and English-Kannada, addressing data scarcity in multilingual NLP.

Contribution

The paper presents a new dependency parsing-based method for creating realistic code-switched data, significantly increasing data volume from minimal input while maintaining grammaticality.

Findings

Algorithm effectively generates grammatically sensible code-switched sentences.

Generated data improves NLP task performance baselines.

Qualitative metrics confirm the quality of synthetic code-switched data.

Abstract

Codeswitching has become one of the most common occurrences across multilingual speakers of the world, especially in countries like India which encompasses around 23 official languages with the number of bilingual speakers being around 300 million. The scarcity of Codeswitched data becomes a bottleneck in the exploration of this domain with respect to various Natural Language Processing (NLP) tasks. We thus present a novel algorithm which harnesses the syntactic structure of English grammar to develop grammatically sensible Codeswitched versions of English-Hindi, English-Marathi and English-Kannada data. Apart from maintaining the grammatical sanity to a great extent, our methodology also guarantees abundant generation of data from a minuscule snapshot of given data. We use multiple datasets to showcase the capabilities of our algorithm while at the same time we assess the quality of generated Codeswitched data using some qualitative metrics along with providing baseline results for couple of NLP tasks.