Forget NLI, Use a Dictionary: Zero-Shot Topic Classification for
  Low-Resource Languages with Application to Luxembourgish
Fred Philippy Shohreh Haddadan Siwen Guo
In NLP, zero-shot classification (ZSC) is the task of assigning labels to
textual data without any labeled examples for the target classes. A common
method for ZSC is to fine-tune a language model on a Natural Language Inference
(NLI) dataset and then use it to infer the entailment between the input
document and the target labels. However, this approach faces certain
challenges, particularly for languages with limited resources. In this paper,
we propose an alternative solution that leverages dictionaries as a source of
data for ZSC. We focus on Luxembourgish, a low-resource language spoken in
Luxembourg, and construct two new topic relevance classification datasets based
on a dictionary that provides various synonyms, word translations and example
sentences. We evaluate the usability of our dataset and compare it with the
NLI-based approach on two topic classification tasks in a zero-shot manner. Our
results show that by using the dictionary-based dataset, the trained models
outperform the ones following the NLI-based approach for ZSC. While we focus on
a single low-resource language in this study, we believe that the efficacy of
our approach can also transfer to other languages where such a dictionary is
available.