Finding Challenging Metaphors that Confuse Pretrained Language Models
Yucheng Li Frank Guerin Chenghua Lin
Metaphors are considered to pose challenges for a wide spectrum of NLP tasks.
This gives rise to the area of computational metaphor processing. However, it
remains unclear what types of metaphors challenge current state-of-the-art
models. In this paper, we test various NLP models on the VUA metaphor dataset
and quantify to what extent metaphors affect models' performance on various
downstream tasks. Analysis reveals that VUA includes a large number of
metaphors that pose little difficulty to downstream tasks. We would like to
shift the attention of researchers away from these metaphors to instead focus
on challenging metaphors. To identify hard metaphors, we propose an automatic
pipeline that identifies metaphors that challenge a particular model. Our
analysis demonstrates that our detected hard metaphors contrast significantly
with VUA and reduce the accuracy of machine translation by 16\%, QA performance
by 4\%, NLI by 7\%, and metaphor identification recall by over 14\% for various
popular NLP systems.