# Spoken dialect identification in Twitter using a multi-filter architecture

arXiv: https://arxiv.org/abs/2006.03564 · HTML: https://mlsanity.com/paper/2006.03564/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2020.

## Built on by (most cited first, 1 of 1)

- [Location-based Twitter Filtering for the Creation of Low-Resource Language Datasets in Indonesian Local Languages](https://mlsanity.com/paper/2206.07238/) (2022)

## Abstract (arXiv)

This paper presents our approach for SwissText & KONVENS 2020 shared task 2, which is a multi-stage neural model for Swiss German (GSW) identification on Twitter. Our model outputs either GSW or non-GSW and is not meant to be used as a generic language identifier. Our architecture consists of two independent filters where the first one favors recall, and the second one filter favors precision (both towards GSW). Moreover, we do not use binary models (GSW vs. not-GSW) in our filters but rather a multi-class classifier with GSW being one of the possible labels. Our model reaches F1-score of 0.982 on the test set of the shared task.