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Evaluating Pixel Language Models on Non-Standardized Languages

Alberto Muñoz-Ortiz, Verena Blaschke, Barbara Plank

TL;DR

This study investigates pixel-based language modeling as a solution to dialectal variability that hampers token-based NLP. By rendering text as 16x16 patches processed by a Vision Transformer, the PIXEL approach avoids vocabulary expansion and shows robust zero-shot transfer from Standard German to several dialects, particularly in syntactic tasks and intent detection. Across four downstream tasks, PIXEL generally surpasses token-based BERT on dialects for POS tagging, dependency parsing, and some semantic tasks, yet underperforms in topic classification and standard German settings. The findings highlight the potential of patch-based, language-agnostic representations for handling orthographic noise and non-standard varieties, with implications for low-resource and multilingual NLP, albeit with computational and data-availability considerations.

Abstract

We explore the potential of pixel-based models for transfer learning from standard languages to dialects. These models convert text into images that are divided into patches, enabling a continuous vocabulary representation that proves especially useful for out-of-vocabulary words common in dialectal data. Using German as a case study, we compare the performance of pixel-based models to token-based models across various syntactic and semantic tasks. Our results show that pixel-based models outperform token-based models in part-of-speech tagging, dependency parsing and intent detection for zero-shot dialect evaluation by up to 26 percentage points in some scenarios, though not in Standard German. However, pixel-based models fall short in topic classification. These findings emphasize the potential of pixel-based models for handling dialectal data, though further research should be conducted to assess their effectiveness in various linguistic contexts.

Evaluating Pixel Language Models on Non-Standardized Languages

TL;DR

This study investigates pixel-based language modeling as a solution to dialectal variability that hampers token-based NLP. By rendering text as 16x16 patches processed by a Vision Transformer, the PIXEL approach avoids vocabulary expansion and shows robust zero-shot transfer from Standard German to several dialects, particularly in syntactic tasks and intent detection. Across four downstream tasks, PIXEL generally surpasses token-based BERT on dialects for POS tagging, dependency parsing, and some semantic tasks, yet underperforms in topic classification and standard German settings. The findings highlight the potential of patch-based, language-agnostic representations for handling orthographic noise and non-standard varieties, with implications for low-resource and multilingual NLP, albeit with computational and data-availability considerations.

Abstract

We explore the potential of pixel-based models for transfer learning from standard languages to dialects. These models convert text into images that are divided into patches, enabling a continuous vocabulary representation that proves especially useful for out-of-vocabulary words common in dialectal data. Using German as a case study, we compare the performance of pixel-based models to token-based models across various syntactic and semantic tasks. Our results show that pixel-based models outperform token-based models in part-of-speech tagging, dependency parsing and intent detection for zero-shot dialect evaluation by up to 26 percentage points in some scenarios, though not in Standard German. However, pixel-based models fall short in topic classification. These findings emphasize the potential of pixel-based models for handling dialectal data, though further research should be conducted to assess their effectiveness in various linguistic contexts.

Paper Structure

This paper contains 19 sections, 2 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Pixel-based models
  3. Experiments
  4. Setup
  5. German non-standard varieties
  6. Syntactic tasks
  7. Data
  8. Results
  9. Accuracy per POS tag
  10. LAS per dependency length
  11. Topic Classification
  12. Data
  13. Results
  14. Intent Detection
  15. Data
  16. ...and 4 more sections

Figures (2)

  • Figure 1: "Welcome!" in Standard German (a) and the Swiss German Bern dialect (b) tokenized using DBMDZ German BERT and rendered and split in patches by PIXEL. Standard German is tokenized in a more meaningful way, whereas the Bernese dialect form results in multiple non-meaningful sub-tokens due to variations in spelling.
  • Figure 2: Labelled attachment scores (in %) for different dependency distances for models trained on German GSD.