Proceedings of the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

Abstract

While various approaches to domain adaptation exist, the majority of them requires knowledge of the target domain, and additional data, preferably labeled.For a language like English, it is often feasible to match most of those conditions, but in low-resource languages, it presents a problem.We explore the situation when neither data nor other information about the target domain is available.We use two samples of Danish, a low-resource language, from the consumer review domain (film vs. company reviews) in a sentiment analysis task.We observe dramatic performance drops when moving from one domain to the other.We then introduce a simple offline method that makes models more robust towards unseen domains, and observe relative improvements of more than 50%.