Multi-VALUE: A Framework for Cross-Dialectal English NLP

TL;DR

This paper introduces Multi-VALUE, a comprehensive framework for evaluating and improving the dialect invariance of English NLP systems across 50 dialects using synthetic data generation and benchmarking.

Contribution

It presents a novel rule-based translation system for 50 English dialects, enabling stress testing and data augmentation to enhance dialect robustness in NLP models.

Findings

Significant performance gaps on non-standard dialects identified

Data augmentation with Multi-VALUE improves model robustness

New gold-standard dialectal datasets released for evaluation

Abstract

Dialect differences caused by regional, social, and economic factors cause performance discrepancies for many groups of language technology users. Inclusive and equitable language technology must critically be dialect invariant, meaning that performance remains constant over dialectal shifts. Current systems often fall short of this ideal since they are designed and tested on a single dialect: Standard American English (SAE). We introduce a suite of resources for evaluating and achieving English dialect invariance. The resource is called Multi-VALUE, a controllable rule-based translation system spanning 50 English dialects and 189 unique linguistic features. Multi-VALUE maps SAE to synthetic forms of each dialect. First, we use this system to stress tests question answering, machine translation, and semantic parsing. Stress tests reveal significant performance disparities for leading models on non-standard dialects. Second, we use this system as a data augmentation technique to improve the dialect robustness of existing systems. Finally, we partner with native speakers of Chicano and Indian English to release new gold-standard variants of the popular CoQA task. To execute the transformation code, run model checkpoints, and download both synthetic and gold-standard dialectal benchmark datasets, see http://value-nlp.org.