DIALECTBENCH: NLP Benchmark for Dialects and Varieties

Core Concepts

DIALECTBENCH is the first comprehensive benchmark for evaluating NLP systems on language varieties, highlighting performance disparities and promoting research on non-standard dialects.

Abstract

1. Introduction

Large-scale multilingual benchmarks like XTREME have focused on standard language varieties.
DIALECTBENCH aims to fill the gap by including non-standard dialects and language varieties.
Provides evidence of performance disparities between standard and non-standard language varieties.
2. DIALECTBENCH

Unifies dialectal datasets across tasks for research on language varieties.
Includes variety selection, cluster mapping, and task diversity.
Evaluates models using in-cluster fine-tuning, zero-shot transfer, and combined fine-tuning.
3. Experiments

Evaluates models like mBERT, XLM-R, and Mistral 7B across tasks.
Utilizes various training approaches like in-variety fine-tuning and zero-shot evaluation.
4. Results

Reports maximum obtainable scores for different tasks across language clusters.
Highlights performance gaps between high-resource and low-resource varieties within clusters.
5. Discussion

Discusses model hyperparameter tuning challenges and positive zero-shot transfer for Latin script varieties.
Evaluates LLM performance via in-context learning and addresses limitations in evaluation metrics.
6. Conclusion

Proposes future improvements for DIALECTBENCH to enhance data quality and expand task coverage.

Stats

DIALECTBENCHは、281の言語バリエーションをカバーする大規模なNLPベンチマークです。

Quotes

"Language technologies should be judged on their usefulness in real-world use cases." - Fahim Faisal et al.

Key Insights Distilled From

DIALECTBENCH

by Fahim Faisal... at arxiv.org 03-19-2024

https://arxiv.org/pdf/2403.11009.pdf

Deeper Inquiries

How can DIALECTBENCH be improved to address data scarcity issues?

DIALECTBENCH can be enhanced in several ways to tackle data scarcity problems. Firstly, researchers can collaborate to curate and contribute more dialectal datasets across various tasks. This collaborative effort will help in expanding the benchmark's coverage of language varieties. Additionally, efforts should be made to create high-quality comparable data for tasks that lack sufficient training examples. This could involve utilizing parallel corpora or generating translation-based task data for a fairer evaluation of models on low-resource varieties.

What are the implications of the observed performance disparities between high-resource and low-resource language varieties?

The performance disparities between high-resource and low-resource language varieties have significant implications. These disparities highlight the challenges faced by NLP systems when dealing with non-standard dialects or less-resourced languages. It underscores the need for developing robust models that can effectively handle linguistic variations across different language varieties. Addressing these disparities is crucial for ensuring equitable access to NLP technologies for all linguistic communities.

How can the findings of DIALECTBENCH contribute to advancing NLP research beyond benchmarking?

The findings of DIALECTBENCH offer valuable insights into the performance discrepancies among diverse language varieties, shedding light on areas where current NLP systems may fall short. By identifying these gaps, researchers can focus on developing more inclusive and effective models that cater to a wider range of linguistic diversity. Moreover, these findings provide a foundation for future research endeavors aimed at improving multilingual capabilities, addressing dialectal variations, and enhancing cross-lingual transfer learning techniques in NLP applications beyond traditional benchmarks.