Automatic Identification of Motivation for Code-Switching in Speech Transcripts

TL;DR

This paper introduces a system that automatically identifies diverse motivations behind code-switching in speech transcripts, achieving 75% accuracy and demonstrating cross-lingual adaptability to Hindi-English.

Contribution

It presents the first system for automatic motivation identification in code-switching, along with a new annotated dataset and cross-lingual transfer capabilities.

Findings

Achieved 75% accuracy in identifying motivations in Spanish-English speech

System can be adapted to Hindi-English with 66% accuracy

Introduced a new dataset with annotated motivations for code-switching

Abstract

Code-switching, or switching between languages, occurs for many reasons and has important linguistic, sociological, and cultural implications. Multilingual speakers code-switch for a variety of purposes, such as expressing emotions, borrowing terms, making jokes, introducing a new topic, etc. The reason for code-switching may be quite useful for analysis, but is not readily apparent. To remedy this situation, we annotate a new dataset of motivations for code-switching in Spanish-English. We build the first system (to our knowledge) to automatically identify a wide range of motivations that speakers code-switch in everyday speech, achieving an accuracy of 75% across all motivations. Additionally, we show that the system can be adapted to new language pairs, achieving 66% accuracy on a new language pair (Hindi-English), demonstrating the cross-lingual applicability of our annotation scheme