mlsanityData to 7 Oct 2026

What Drives the Use of Metaphorical Language? Negative Insights from Abstractness, Affect, Discourse Coherence and Contextualized Word Representations

arXiv 2205.11113 ↗First posted 23 May 2022cs.CLcited by 3

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Abstract (arXiv)

Given a specific discourse, which discourse properties trigger the use of metaphorical language, rather than using literal alternatives? For example, what drives people to say "grasp the meaning" rather than "understand the meaning" within a specific context? Many NLP approaches to metaphorical language rely on cognitive and (psycho-)linguistic insights and have successfully defined models of discourse coherence, abstractness and affect. In this work, we build five simple models relying on established cognitive and linguistic properties -- frequency, abstractness, affect, discourse coherence and contextualized word representations -- to predict the use of a metaphorical vs. synonymous literal expression in context. By comparing the models' outputs to human judgments, our study indicates that our selected properties are not sufficient to systematically explain metaphorical vs. literal language choices.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 9 of its 69 references that are in the map. First posted on arXiv 23 May 2022.

Builds on

9 of 69 references.

References · 9

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2019BERT Rediscovers the Classical NLP Pipelinecited by 1,046
  4. 2019What Does BERT Look At? An Analysis of BERT's Attentioncited by 994
  5. 2019To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Taskscited by 167
  6. 2020Learning and Evaluating Emotion Lexicons for 91 Languagescited by 13
  7. 2018Emotion Representation Mapping for Automatic Lexicon Construction (Mostly) Performs on Human Levelcited by 7
  8. 2022Features of Perceived Metaphoricity on the Discourse Level: Abstractness and Emotionalitycited by 4
  9. 2019Exploring Fine-Tuned Embeddings that Model Intensifiers for Emotion Analysiscited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 0; 2026: 1.

3 papers.