Generating Continuations in Multilingual Idiomatic Contexts

• URL: http://arxiv.org/abs/2310.20195v2
• Date: Sat, 4 Nov 2023 04:15:18 GMT
• Title: Generating Continuations in Multilingual Idiomatic Contexts
• Authors: Rhitabrat Pokharel, Ameeta Agrawal
• Abstract summary: We test the ability of generative language models (LMs) in understanding nuanced language containing non-compositional figurative text. We conduct experiments using datasets in two distinct languages (English and Portuguese) under three different training settings. Our results suggest that the models are only slightly better at generating continuations for literal contexts than idiomatic contexts, with exceedingly small margins.
• Score: 2.0849578298972835
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The ability to process idiomatic or literal multiword expressions is a crucial aspect of understanding and generating any language. The task of generating contextually relevant continuations for narratives containing idiomatic (or literal) expressions can allow us to test the ability of generative language models (LMs) in understanding nuanced language containing non-compositional figurative text. We conduct a series of experiments using datasets in two distinct languages (English and Portuguese) under three different training settings (zero-shot, few-shot, and fine-tuned). Our results suggest that the models are only slightly better at generating continuations for literal contexts than idiomatic contexts, with exceedingly small margins. Furthermore, the models studied in this work perform equally well across both languages, indicating the robustness of generative models in performing this task.

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