Work

Task-Specific Pre-Training and Cross Lingual Transfer for Code-Switched Data

Year: 2021

Type: preprint

Abstract: Using task-specific pre-training and leveraging cross-lingual transfer are two of the most popular ways to handle code-switched data. In this paper, we aim to compare the effects of both for the task of sentiment analysis. We work with two Dravidian Code-Switched languages - Tamil-Engish and Malayalam-English and four different BERT based models. We compare the effects of task-specific pre-training and cross-lingual transfer and find that task-specific pre-training results in superior zero-shot and supervised performance when compared to performance achieved by leveraging cross-lingual transfer from multilingual BERT models.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2102.12407

Authors: Gupta, Akshat, Rallabandi, Sai Krishna, Black, Alan

Language: English

Topic: Sentiment Analysis and Opinion Mining

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W3131976433

Cites: 10

Cited by: 7

Related to: 10