# Language Model Adaptation for Language and Dialect Identification of Text

## TL;DR

This paper introduces an unsupervised language model adaptation technique to improve language and dialect identification accuracy, demonstrating significant performance gains on shared task datasets.

## Contribution

It presents a novel unsupervised adaptation method integrated into HeLI 2.0, enhancing language identification performance especially on out-of-domain data.

## Key findings

- Higher F1-scores than previous HeLI and other systems
- Effective in out-of-domain language identification scenarios
- Applicable to various language and dialect identification tasks

## Abstract

This article describes an unsupervised language model adaptation approach that can be used to enhance the performance of language identification methods. The approach is applied to a current version of the HeLI language identification method, which is now called HeLI 2.0. We describe the HeLI 2.0 method in detail. The resulting system is evaluated using the datasets from the German dialect identification and Indo-Aryan language identification shared tasks of the VarDial workshops 2017 and 2018. The new approach with language identification provides considerably higher F1-scores than the previous HeLI method or the other systems which participated in the shared tasks. The results indicate that unsupervised language model adaptation should be considered as an option in all language identification tasks, especially in those where encountering out-of-domain data is likely.