  Transformer-based models achieve impressive performance on numerous Natural
Language Inference (NLI) benchmarks when trained on respective training
datasets. However, in certain cases, training samples may not be available or
collecting them could be time-consuming and resource-intensive. In this work,
we address the above challenge and present an explorative study on unsupervised
NLI, a paradigm in which no human-annotated training samples are available. We
investigate it under three settings: PH, P, and NPH that differ in the extent
of unlabeled data available for learning. As a solution, we propose a
procedural data generation approach that leverages a set of sentence
transformations to collect PHL (Premise, Hypothesis, Label) triplets for
training NLI models, bypassing the need for human-annotated training data.
Comprehensive experiments with several NLI datasets show that the proposed
approach results in accuracies of up to 66.75%, 65.9%, 65.39% in PH, P, and NPH
settings respectively, outperforming all existing unsupervised baselines.
Furthermore, fine-tuning our model with as little as ~0.1% of the
human-annotated training dataset (500 instances) leads to 12.2% higher accuracy
than the model trained from scratch on the same 500 instances. Supported by
this superior performance, we conclude with a recommendation for collecting
high-quality task-specific data.
