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

arXiv: https://arxiv.org/abs/2205.11113 · HTML: https://mlsanity.com/paper/2205.11113/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 69 references that are in the map.
- First posted on arXiv 23 May 2022.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BERT Rediscovers the Classical NLP Pipeline](https://mlsanity.com/paper/1905.05950/) (2019)
- [What Does BERT Look At? An Analysis of BERT's Attention](https://mlsanity.com/paper/1906.04341/) (2019)
- [To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Tasks](https://mlsanity.com/paper/1903.05987/) (2019)
- [Learning and Evaluating Emotion Lexicons for 91 Languages](https://mlsanity.com/paper/2005.05672/) (2020)
- [Emotion Representation Mapping for Automatic Lexicon Construction (Mostly) Performs on Human Level](https://mlsanity.com/paper/1806.08890/) (2018)
- [Features of Perceived Metaphoricity on the Discourse Level: Abstractness and Emotionality](https://mlsanity.com/paper/2205.08939/) (2022)
- [Exploring Fine-Tuned Embeddings that Model Intensifiers for Emotion Analysis](https://mlsanity.com/paper/1904.03164/) (2019)

## Built on by (most cited first, 3 of 3)

- [Representation Of Lexical Stylistic Features In Language Models' Embedding Space](https://mlsanity.com/paper/2305.18657/) (2023)
- [A framework for annotating and modelling intentions behind metaphor use](https://mlsanity.com/paper/2407.03952/) (2024)
- [Floating or Suggesting Ideas? A Large-Scale Contrastive Analysis of Metaphorical and Literal Verb-Object Constructions](https://mlsanity.com/paper/2604.08275/) (2026)

## 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.