  Historically, researchers and consumers have noticed a decrease in quality
when applying NLP tools to minority variants of languages (i.e. Puerto Rican
Spanish or Swiss German), but studies exploring this have been limited to a
select few languages. Additionally, past studies have mainly been conducted in
a monolingual context, so cross-linguistic trends have not been identified and
tied to external factors. In this work, we conduct a comprehensive evaluation
of the most influential, state-of-the-art large language models (LLMs) across
two high-use applications, machine translation and automatic speech
recognition, to assess their functionality on the regional dialects of several
high- and low-resource languages. Additionally, we analyze how the regional
dialect gap is correlated with economic, social, and linguistic factors. The
impact of training data, including related factors like dataset size and its
construction procedure, is shown to be significant but not consistent across
models or languages, meaning a one-size-fits-all approach cannot be taken in
solving the dialect gap. This work will lay the foundation for furthering the
field of dialectal NLP by laying out evident disparities and identifying
possible pathways for addressing them through mindful data collection.
