JamPatoisNLI: A Jamaican Patois Natural Language Inference Dataset
This work addresses the lack of NLP resources for creole languages, which are widely spoken but underserved, though it is incremental as it builds on existing cross-lingual transfer methods.
The authors tackled the problem of natural language inference for low-resource creole languages by creating JamPatoisNLI, the first dataset for Jamaican Patois, and found that few-shot learning on this dataset yields considerably better results than for unrelated low-resource languages.
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.