Multi-lingual and Multi-cultural Figurative Language Understanding
Anubha Kabra Emmy Liu Simran Khanuja Alham Fikri Aji Genta Indra Winata Samuel Cahyawijaya Anuoluwapo Aremu Perez Ogayo Graham Neubig
Figurative language permeates human communication, but at the same time is
relatively understudied in NLP. Datasets have been created in English to
accelerate progress towards measuring and improving figurative language
processing in language models (LMs). However, the use of figurative language is
an expression of our cultural and societal experiences, making it difficult for
these phrases to be universally applicable. In this work, we create a
figurative language inference dataset, \datasetname, for seven diverse
languages associated with a variety of cultures: Hindi, Indonesian, Javanese,
Kannada, Sundanese, Swahili and Yoruba. Our dataset reveals that each language
relies on cultural and regional concepts for figurative expressions, with the
highest overlap between languages originating from the same region. We assess
multilingual LMs' abilities to interpret figurative language in zero-shot and
few-shot settings. All languages exhibit a significant deficiency compared to
English, with variations in performance reflecting the availability of
pre-training and fine-tuning data, emphasizing the need for LMs to be exposed
to a broader range of linguistic and cultural variation during training.