FLUTE: Figurative Language Understanding through Textual Explanations
This addresses the problem of assessing genuine understanding of figurative language for NLP researchers, though it is incremental as it extends existing explanation-based dataset methods to a new domain.
The authors tackled the lack of explanation-based datasets for figurative language understanding by releasing FLUTE, a dataset of 9,000 figurative NLI instances with explanations across four categories, collected using a model-in-the-loop framework with GPT-3 and human annotators.
Figurative language understanding has been recently framed as a recognizing textual entailment (RTE) task (a.k.a. natural language inference, or NLI). However, similar to classical RTE/NLI datasets, the current benchmarks suffer from spurious correlations and annotation artifacts. To tackle this problem, work on NLI has built explanation-based datasets such as e-SNLI, allowing us to probe whether language models are right for the right reasons.Yet no such data exists for figurative language, making it harder to assess genuine understanding of such expressions. To address this issue, we release FLUTE, a dataset of 9,000 figurative NLI instances with explanations, spanning four categories: Sarcasm, Simile, Metaphor, and Idioms. We collect the data through a model-in-the-loop framework based on GPT-3, crowd workers, and expert annotators. We show how utilizing GPT-3 in conjunction with human annotators (novices and experts) can aid in scaling up the creation of datasets even for such complex linguistic phenomena as figurative language. The baseline performance of the T5 model fine-tuned on FLUTE shows that our dataset can bring us a step closer to developing models that understand figurative language through textual explanations.