# Subword-Level Language Identification for Intra-Word Code-Switching

## TL;DR

This paper introduces a subword-level language identification approach for intra-word code-switching, using a segmental RNN to better handle morphologically complex mixed words, with promising results on new datasets.

## Contribution

It extends language ID to subword units for intra-word CS and proposes a segmental RNN model, improving identification of mixed morphemes within words.

## Key findings

- Outperforms baselines on mixed word identification
- Effective on Spanish--Wixarika dataset
- Comparable performance on German--Turkish dataset

## Abstract

Language identification for code-switching (CS), the phenomenon of alternating between two or more languages in conversations, has traditionally been approached under the assumption of a single language per token. However, if at least one language is morphologically rich, a large number of words can be composed of morphemes from more than one language (intra-word CS). In this paper, we extend the language identification task to the subword-level, such that it includes splitting mixed words while tagging each part with a language ID. We further propose a model for this task, which is based on a segmental recurrent neural network. In experiments on a new Spanish--Wixarika dataset and on an adapted German--Turkish dataset, our proposed model performs slightly better than or roughly on par with our best baseline, respectively. Considering only mixed words, however, it strongly outperforms all baselines.