  JamPatoisNLI provides the first dataset for natural language inference in a
creole language, Jamaican Patois. Many of the most-spoken low-resource
languages are creoles. These languages commonly have a lexicon derived from a
major world language and a distinctive grammar reflecting the languages of the
original speakers and the process of language birth by creolization. This gives
them a distinctive place in exploring the effectiveness of transfer from large
monolingual or multilingual pretrained models. While our work, along with
previous work, shows that transfer from these models to low-resource languages
that are unrelated to languages in their training set is not very effective, we
would expect stronger results from transfer to creoles. Indeed, our experiments
show considerably better results from few-shot learning of JamPatoisNLI than
for such unrelated languages, and help us begin to understand how the unique
relationship between creoles and their high-resource base languages affect
cross-lingual transfer. JamPatoisNLI, which consists of naturally-occurring
premises and expert-written hypotheses, is a step towards steering research
into a traditionally underserved language and a useful benchmark for
understanding cross-lingual NLP.
