The Secret of Metaphor on Expressing Stronger Emotion
- URL: http://arxiv.org/abs/2301.13042v1
- Date: Mon, 30 Jan 2023 16:36:02 GMT
- Title: The Secret of Metaphor on Expressing Stronger Emotion
- Authors: Yucheng Li, Frank Guerin, Chenghua Lin
- Abstract summary: This paper conducts the first study in exploring how metaphors convey stronger emotion than their literal counterparts.
The more specific property of metaphor can be one of the reasons for metaphors' superiority in emotion expression.
In addition, we observe specificity is crucial in literal language as well, as literal language can express stronger emotion by making it more specific.
- Score: 16.381658893164538
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Metaphors are proven to have stronger emotional impact than literal
expressions. Although this conclusion is shown to be promising in benefiting
various NLP applications, the reasons behind this phenomenon are not well
studied. This paper conducts the first study in exploring how metaphors convey
stronger emotion than their literal counterparts. We find that metaphors are
generally more specific than literal expressions. The more specific property of
metaphor can be one of the reasons for metaphors' superiority in emotion
expression. When we compare metaphors with literal expressions with the same
specificity level, the gap of emotion expressing ability between both reduces
significantly. In addition, we observe specificity is crucial in literal
language as well, as literal language can express stronger emotion by making it
more specific.
Related papers
- Verifying Claims About Metaphors with Large-Scale Automatic Metaphor Identification [14.143299702954023]
This study entails a large-scale, corpus-based analysis of certain existing claims about verb metaphors, by applying metaphor detection to sentences extracted from Common Crawl.
The verification results indicate that the direct objects of verbs used as metaphors tend to have lower degrees of concreteness, imageability, and familiarity, and that metaphors are more likely to be used in emotional and subjective sentences.
arXiv Detail & Related papers (2024-04-01T10:17:45Z) - That was the last straw, we need more: Are Translation Systems Sensitive
to Disambiguating Context? [64.38544995251642]
We study semantic ambiguities that exist in the source (English in this work) itself.
We focus on idioms that are open to both literal and figurative interpretations.
We find that current MT models consistently translate English idioms literally, even when the context suggests a figurative interpretation.
arXiv Detail & Related papers (2023-10-23T06:38:49Z) - LMs stand their Ground: Investigating the Effect of Embodiment in
Figurative Language Interpretation by Language Models [0.0]
Figurative language is a challenge for language models since its interpretation deviates from their conventional order and meaning.
Yet, humans can easily understand and interpret metaphors as they can be derived from embodied metaphors.
This study shows how larger language models perform better at interpreting metaphoric sentences when the action of the metaphorical sentence is more embodied.
arXiv Detail & Related papers (2023-05-05T11:44:12Z) - Speech Synthesis with Mixed Emotions [77.05097999561298]
We propose a novel formulation that measures the relative difference between the speech samples of different emotions.
We then incorporate our formulation into a sequence-to-sequence emotional text-to-speech framework.
At run-time, we control the model to produce the desired emotion mixture by manually defining an emotion attribute vector.
arXiv Detail & Related papers (2022-08-11T15:45:58Z) - What Drives the Use of Metaphorical Language? Negative Insights from
Abstractness, Affect, Discourse Coherence and Contextualized Word
Representations [13.622570558506265]
Given a specific discourse, which discourse properties trigger the use of metaphorical language, rather than using literal alternatives?
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 to predict the use of a metaphorical vs. synonymous literal expression in context.
arXiv Detail & Related papers (2022-05-23T08:08:53Z) - Features of Perceived Metaphoricity on the Discourse Level: Abstractness
and Emotionality [13.622570558506265]
Research on metaphorical language has shown ties between abstractness and emotionality with regard to metaphoricity.
This paper explores which textual and perceptual features human annotators perceive as important for the metaphoricity of discourse.
arXiv Detail & Related papers (2022-05-18T14:09:10Z) - It's not Rocket Science : Interpreting Figurative Language in Narratives [48.84507467131819]
We study the interpretation of two non-compositional figurative languages (idioms and similes)
Our experiments show that models based solely on pre-trained language models perform substantially worse than humans on these tasks.
We additionally propose knowledge-enhanced models, adopting human strategies for interpreting figurative language.
arXiv Detail & Related papers (2021-08-31T21:46:35Z) - A Circular-Structured Representation for Visual Emotion Distribution
Learning [82.89776298753661]
We propose a well-grounded circular-structured representation to utilize the prior knowledge for visual emotion distribution learning.
To be specific, we first construct an Emotion Circle to unify any emotional state within it.
On the proposed Emotion Circle, each emotion distribution is represented with an emotion vector, which is defined with three attributes.
arXiv Detail & Related papers (2021-06-23T14:53:27Z) - Interpreting Verbal Metaphors by Paraphrasing [12.750941606061877]
We show that our paraphrasing method significantly outperforms the state-of-the-art baseline.
We also demonstrate that our method can help a machine translation system improve its accuracy in translating English metaphors to 8 target languages.
arXiv Detail & Related papers (2021-04-07T21:00:23Z) - Metaphoric Paraphrase Generation [58.592750281138265]
We use crowdsourcing to evaluate our results, as well as developing an automatic metric for evaluating metaphoric paraphrases.
We show that while the lexical replacement baseline is capable of producing accurate paraphrases, they often lack metaphoricity.
Our metaphor masking model excels in generating metaphoric sentences while performing nearly as well with regard to fluency and paraphrase quality.
arXiv Detail & Related papers (2020-02-28T16:30:33Z) - Annotation of Emotion Carriers in Personal Narratives [69.07034604580214]
We are interested in the problem of understanding personal narratives (PN) - spoken or written - recollections of facts, events, and thoughts.
In PN, emotion carriers are the speech or text segments that best explain the emotional state of the user.
This work proposes and evaluates an annotation model for identifying emotion carriers in spoken personal narratives.
arXiv Detail & Related papers (2020-02-27T15:42:39Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.