What Drives the Use of Metaphorical Language?
Negative Insights from Abstractness, Affect, Discourse Coherence andContextualized Word Representations
Prisca Piccirilli and Sabine Schulte im Walde
Institute for Natural Language Processing, University of Stuttgart, Germany{prisca.piccirilli,schulte}@ims.uni-stuttgart.de

Abstract
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 havesuccessfully defined models of discourse coherence, abstractness and affect. In this work, webuild five simple models relying on establishedcognitive and linguistic properties – frequency,
abstractness, affect, discourse coherence andcontextualized word representations – to predict the use of a metaphorical vs. synonymous
literal expression in context. By comparing themodels’ outputs to human judgments, our study
indicates that our selected properties are not sufficient to systematically explain metaphoricalvs. literal language choices.

1

Introduction

Metaphors are "not just nice", but represent a "necessary" element of everyday thought and communication (Ortony, 1975; Lakoff and Johnson, 1980;
van den Broek, 1981; Schäffner, 2004, i.a.), and are
ubiquitous in natural language text corpora (Gedigian et al., 2006; Shutova and Teufel, 2010; Steenet al., 2010, i.a.). From the perspective of natural
language processing (NLP), automatic approaches
to metaphor processing are therefore important forany task that requires natural language understanding, and NLP has been concerned with the detection(Köper and Schulte im Walde, 2016; Alnafesah
et al., 2020; Ehren et al., 2020; Dankers et al., 2020,i.a.), the interpretation (Shutova, 2010; Bizzoni and
Lappin, 2018; Mao et al., 2018, i.a.) and, the generation (Stowe et al., 2021; Zhou et al., 2021) ofmetaphors.1
1

See Tong et al. (2021) for a systematic, comprehensive
review and discussion of the most recent metaphor processing
systems and datasets.

As to our knowledge, however, no study so far
has raised the question of WHY a metaphorical expression is used within a specific discourse, rather
than an equally plausible literal alternative. For example, consider the discourse in table 1, where boththe metaphorical expression grasp the meaning andits synonymous literal alternative understand themeaning seem equally acceptable (Piccirilli and
Schulte im Walde, 2021, 2022). What are the factors priming for one use or the other? Are there
cues within the discourse which influence the selection of one usage over the other? To which
extent can computational approaches based on discourse properties model human behavior regarding
the choice between synonymous metaphorical vs.
literal language usage?
According to psycholinguistics and computational linguistics research, the processing of words
is a function of their frequency of occurrencein the language (van Jaarsveld and Rattink, 1988;
Wittmann et al., 2017, i.a.). Is the choice between
a metaphorical and a literal expression therefore
just an effect of frequency? In contrast, conceptual metaphor theory establishes metaphorical language as a figurative device for transferring knowledge from a concrete domain to a more abstract
domain (Lakoff and Johnson, 1980), and the hypothesis that metaphorical usages correlate with theabstractness of the context has been supported
in numerous NLP studies on automatic metaphor
identification (Turney et al., 2011; Tsvetkov et al.,2013; Köper and Schulte im Walde, 2016; Alnafesah et al., 2020; Hall Maudslay et al., 2020). Affecthas also been explored with regard to metaphoricity. Not only has metaphorical language been foundto carry a stronger emotional load than literal language (Blanchette and Dunbar, 2001; Crawford,2009), but metaphorical words and sentences arealso judged to be "more emotionally engaging"than their synonymous literal paraphrases (Citronand Goldberg, 2014; Mohammad et al., 2016), andThis wasn’t just a play on words, rather it was a demand that they should ’maintain a consistency between their words andtheir actions’. But I agree, that still does not absolve them from the need to speak truth to power. In our times when people
spend so much time with TV and the internet, do they have the interest and time to read poetry? Many people believe that itis difficult to read poetry. Can everyone [grasp / understand the meaning] of a good poem, or is a skill necessary?
Table 1: Example of a discourse from the dataset introduced by Piccirilli and Schulte im Walde (2021). Both the literal expression
understand meaning and its metaphorical counterpart grasp meaning seem equally acceptable in the discourse.

informing NLP models with emotion features has
proven useful for metaphor detection (Gargett and
Barnden, 2015; Köper and Schulte im Walde, 2018;
Dankers et al., 2019). When analyzing metaphorical discourse features (Glucksberg, 1989; Steen,2004) and the interactions of discourse coherencewith the contextual salience of metaphorical vs. literal expressions (Inhoff et al., 1984; Gibbs, 1989;
Giora, 1997; Giora and Fein, 1999; Gibbs, 2002;
Kövecses, 2009), findings are directly connectedwith the theory of discourse cohesion, i.e., the principle that discourse should be a "group of collocated, structured, and coherent sentences" (Halliday and Hasan, 1976; Jurafsky and Martin, 2019).
Models relying on the coherence of lexical semantic discourse structures have therefore been successful in identifying metaphors (Sporleder and Li,
2009; Bogdanova, 2010; Mesgar and Strube, 2016;
Dankers et al., 2020). From a yet different perspective, Transformer-based pretrained language
models (T-PLMs) (Vaswani et al., 2017), pre-trainedon the language modeling task, are able to predictmasked items in context and produce contextual
embeddings accounting for both left and right contexts, and resulting in word representations that are
dynamically informed by the surrounding words.
The rich previous interdisciplinary research on
figurative language seems to agree on tight interactions between metaphorical language detection and properties of the respective discourses,
i.e., cognitive aspects (abstractness and affect),discourse coherence and contextual properties.
Do these discourse properties indeed trigger theuse of a metaphorical vs. its synonymous counterpart? We address this question by exploring
five simple discourse-based models inspired by
the above-mentioned properties, namely frequency,
abstractness, affect, discourse coherence and contextualized word representations. We approachthe question within two studies. First, we explore
discourse features of metaphorical usages occurring in natural language by applying the cognitiveand linguistically-inspired models to existing discourses from the English corpus ukWaC (Baroni

et al., 2009). Then, we zoom into the models’ predictions and evaluate them against human preferences (i.e., annotations) for metaphorical vs. literallanguage within the same discourses. By modelingthe prediction for synonymous metaphorical vs. literal expressions motivated by the above discourse
perspectives, we gain insight into human behavior
for metaphorical vs. literal languages choices.

2

DatasetResearch on figurative language has produced impressive resources on the metaphoricity of lexical items. However, these resources have limitations regarding the specific task we are addressingin this work: (i) the context in which words aremetaphorically used is not large enough, providing
human judgments only on the word-level (Steenet al., 2010) or on the sentence-level (Stefanowitsch, 2008; Shutova and Teufel, 2010; Mohammad et al., 2016), (ii) the target words or expressions are ambiguous, i.e., they may have both ametaphorical and a literal sense (Tsvetkov et al.,2013; Mohler et al., 2016), (iii) they are extended
metaphors (Gibbs, 2006; Martin, 2008), (iv) the
paraphrases to metaphorically-used words are automatically generated and no manual annotations
were provided to evaluate the outputs (Bollegala
and Shutova, 2013; Bizzoni and Lappin, 2018, i.a.).
We therefore use our recently released dataset
specifically designed to investigate the choice of
metaphorical vs. literal expressions in context (Piccirilli and Schulte im Walde, 2021, 2022). It contains a total of 1,000 discourses of five to six sentences (98 words on average), in which the final sentence of each discourse contains either a metaphorical expression or its literal alternative from a pairof synonymous subject–verb (SV) or verb–object(VO) expressions. Table 1 presents an example for
the VO pair grasp/understand meaning. The overall
50 pairs of English expressions were selected from
Shutova (2010) and Mohammad et al. (2016), andfor each of the pairs, we extracted 20 discourses
from the ukWaC (Baroni et al., 2009), 10 of whichcontaining the metaphorical usage (e.g., grasp) and10 of which containing the literal paraphrase (e.g.,understand). To gain insight into human preferences between the selected pairs of metaphorical vs.
literal expressions, we also collected crowdsourced
human judgments for these 1,000 discourses, asking annotators to choose which expression theyfavored given the preceding discourse. This dataset
is therefore optimal for the task at hand, as it provides (i) synonymous metaphorical vs. literal expressions, (ii) at the discourse-level, (iii) manuallyannotated. In the present work, we make use of
the 1,000 discourses containing the original expressions in the ukWaC and the annotators’ choices
for metaphorical vs. literal usages within a subset
of 287 discourses, where 70% or more annotators
agreed on the preference (metaphorical or literal).

3

Models and Experimental Setup

3.1

Prediction models

We approach the task of predicting the use of
metaphorical vs. literal expressions as a prompting
task: given an input prompt2 , the models predictwhether the missing span should be the metaphorical or the literal expression. We apply five models
relying on discourse properties, where each model
approaches the task from a different perspective, togive us insight on which discourse features influence the metaphorical vs. literal selection.
Frequency approach When given the choice between a metaphorical expression and its synonymous counterpart, do we tend to favor the mostfrequent usage? Baseline (Freq.): Our baseline relies on the occurrences of the SV and OV tuples in
the original ukWaC corpus: the model receives theprefix prompt as input, and always outputs the mostfrequent expression of the pair.
Cognitively-inspired approaches The cognitive interaction between abstractness/affect andmetaphorical language raises the question: towhich extent (i) a more abstract discourse and(ii) a more emotionally-loaded discourse favor a
metaphorical usage? Abstractness (Abstr.): We
measure the abstractness of a discourse preceding
the target expression within four settings, basedon the norms from Brysbaert et al. (2014). We
assign abstractness scores to all words (Abstr.all),only nouns (Abstr.n), only verbs (Abstr.v) or only
adjectives (Abstr.adj). We then obtain an overall
2
The input prompt is a prefix prompt for all models except
for BERT, whose input is a cloze prompt.

rating of abstractness for each discourse by computing the median of the respective lexical items’
abstractness scores. For each setting, we use as
threshold the abstractness median of the respective
part-of-speech class, and the model predicts themetaphorical expression if the overall abstractness
score of the discourse is below that threshold (i.e.,
more abstract/less concrete), and the literal counterpart if above (i.e., less abstract/more concrete).
Emotionality (Emo.): We build a model predicting
the target expression based on the emotionality ofthe preceding discourse, which we represent using
the English emotion lexicon from Buechel et al.
(2020). Each lexical item from the preceding discourse is assigned an emotionality score, and the
median represents the overall emotionality score of
the discourse. Appendix A.1 provides details on
the emotionality score.
Discourse coherence approach Is the choiceof an expression from a synonymous pair driven
by the semantic relatedness between the components of that expression and the lexical items in thesurrounding context? We adapt the Lexical Coherence Graph (LCG) introduced by Mesgar andStrube (2016) to measure the semantic relatedness
between the words in the preceding context and thetarget expression contained in the final discourse
sentence: we compute the cosine scores for thetwo components in the SV/VO expressions (i.e., theverb and its argument) and each word in the preceding discourse, relying on contextualized BERT
embeddings (Devlin et al., 2019). The output is agraph connecting the preceding context to both themetaphorical expression and its synonymous alternative; edge weights are represented by the averageof the respecting cosine values. The expression –metaphorical or literal – with the maximum weight(i.e., the largest average cosine score) is selected.
Appendix A.2 provides details on the LCG score.
Contextualized discourse properties How docontextualized word representations prime for theuse of a metaphorical vs. literal preference? Ourquestion triggers a cloze-task style (Taylor, 1953)
and applies the Pre-trained Language Model
BERT (Devlin et al., 2019) in a zero-shot manner.
We give a cloze prompt as input to the model, as
in table 1: we mask the target expression, and themodel selects the most probable answer amongst
the two candidates (metaphorical or literal). We
experiment with both BERTbase and BERTlarge .

3.2

Experimental Setup

We compare our models’ predictions against twogold standards. We first evaluate the models’ outputs against the 1,000 discourses collected from
ukWaC, containing the balanced originally-used
metaphorical or literal expressions (Orig. Data)
to investigate which discourse aspects (abstractness, emotionality, coherence, word representations) might have primed the metaphorical vs. literal selection. We expect the model predictions
to provide insight into the features that influenced
the original speakers’ preferences. We then zoom
into the annotated version of the same data (Anno.
Data), for which participants were asked which
expression, metaphorical or literal, they favoredin the given 287 discourses (cf. Section 2). We
analyze whether (dis-)agreements between humans
are also reflected in the models’ predictions.

4

Results and Analyses

We first analyze the models’ predictions with regard to metaphorical vs. literal usages in the originally-extracted data (Orig. Data), to address thequestion: do cognitive and linguistically-inspired
models reflect metaphorical language usage encountered in natural language corpora? The firstcolumn of figure 1 presents the accuracy score ofeach model with regard to Orig. Data. All models reach around 50% of accuracy; however, they
behave very differently regarding their individual
predictions.3 Figure 1 presents the percentages ofoverlapping output decisions between our five models, revealing interesting insights: we observe 77%
overlapping predictions between the PLMs with thefrequency baseline, suggesting that the majority of
PLM predictions is frequency-driven, which is not
the case for most abstractness settings and emotionality. Emotionality itself correlates with abstractness in all settings but the one where we consider
only adjectives, which is surprising as adjectives
tend to carry a lot of emotions (Mohammad andTurney, 2010; Bostan and Klinger, 2019). The overlap between LCG and BERT reaches 62%, whereas
its correlations with abstractness and emotionality are rather low, suggesting that decisions basedon abstractness and emotionality are different tothose based on word representations and semantic
relatedness. At first sight, our different perspectives seem rather complementary, which is yet tobe verified in future studies.
3

Appendix B.1 presents further prediction differences.

Figure 1: Accuracy scores of each model with regard to theoriginal data (1st column in red) and percentages of overlapping output decisions between models.

Overall, none of our models seems to reliably
predict what is observed in natural language data.
We thus cannot derive what might have triggered
the original speakers to favor a metaphorical or
literal expression over the other.
We then compare our models’ predictions to human behavior: are the proportions of metaphoricalvs. literal predictions from our models similar to human perceptions (Anno. Data)? Figure 2 presents
the proportions of the metaphorical expressions
predicted by four of the models in relation to the
proportions of metaphorical usages favored by thehuman judges.4 If humans and models were making similar decisions, all data points, each representing the proportion of metaphorical uses for apair of expressions, would be on the regression
line. We make different observations depending on
the model in question. As far as frequency is concerned, the model predicts either the metaphoricalor the literal expression (100% or 0% respectively,in the graph). Many literal expressions that are
favored by participants (low x-axis %) seem tocorrelate with their higher occurrences in natural
language corpora, e.g., make vs. throw remark (VO).
However, humans also favor many metaphorical expressions which are less frequently used, e.g., stir
vs. cause excitement (VO) and inversely, a few
metaphorical expressions which are more frequent
than their literal counterparts are not necessarily
favored by people, e.g., poison vs. corrupt mind
(VO). Thus, frequency does not seem to be a systematic factor for metaphorical/literal choices. Regard4

Appendix B.2 shows the graphs for all models.

Figure 2: Proportions of predicted metaphorical expressions for each model, with regard to the proportions of these
metaphorical expressions favored by annotators. Appendix B.2 shows the graphs for all models.

ing abstractness, many expressions fall perfectlyon the regression line, e.g., twist vs. misinterpretword (VO), story grab vs. intrigue (SV), suggestingat first some interactions between the expression
preferences and the abstractness of the respective
discourses. However, the numerous metaphoricalexpressions that are predicted by the model (80%+)
but not by humans, e.g., taste vs. experience freedom (VO), factor shape vs. determine (SV) do notconfirm that hypothesis. Concerning the LCG, thepicture is less clear. Many literal expressions arefavored by both the model and humans, e.g., color
vs. affect judgement (VO), but many metaphoricalusages are preferred by humans when the model
predicts the literal ones, e.g., breathe vs. instill life(VO). Therefore, metaphorical vs. literal coherence
does not seem either to be a determining factor
for metaphorical vs. literal preference, respectively,
which does not support the context-salience hypothesis, where one would expect a metaphoricalexpression to be favored following a metaphorical
discourse, and ditto for a literal expression/literaldiscourse (Kövecses, 2009). Finally, the overall
low metaphorical predictions by the PLM are expressions for which humans provide high metaphorical
proportions, such as abuse vs. drink alcohol (VO).
This suggests a potential lack of metaphorical language representation, in line with our observation
concerning abstractness and emotionality.

5

Discussion and Future Work

Previous research has provided evidence for interactions between metaphorical language and discourse properties, namely frequency, abstractness,
affect, discourse coherence and contextualized
word representations, on the word- and sentence-level. In this work, we took a step further: welooked at the task of predicting the use of a
metaphorical vs. synonymous literal expression
and built models based on the above-mentioned
features on the discourse-level. Our findings showthat these discourse properties do not seem to be
indicative of metaphorical usage.
We propose several directions for future work.
First of all, we considered discourses of around fivesentences, but the decision on the context window
might have an impact on the findings. Further work
exploring the optimal size of preceding context
would be interesting. Another promising direction
might analyze PLMs’ attention mechanisms (Tenney et al., 2019; Clark et al., 2019) on the presented
task, and also explore the extent to which modifying the attention of such models, i.e., fine-tuning(Peters et al., 2019; Zhao and Bethard, 2020), improves their performance to mimic human preferences for metaphorical vs. literal usages. Finally,
we have looked at five features; needless to say that
exploring further discourse properties is necessary,such as co-reference, complexity, aptness, creativity, prototypicality, and the influence of genre andspecific domains (e.g., religious/scientific texts).

6

Conclusion

We suggested five simple models to investigate
WHY humans choose to use a metaphorical expression in a specific discourse. Regardless of the perspectives, our work demonstrates that a range ofpreviously suggested salient discourse properties
do not seem to influence preferences on the choice
between synonymous metaphorical vs. literal expressions. Our findings thus ask for a more nuanced
approach to metaphorical language choices in NLP.

Acknowledgements
We thank the anonymous reviewers for their usefuland quality feedback. This research was supportedby the DFG Research Grant SCHU 2580/4-1 Multimodal Dimensions and Computational Applications of Abstractness.

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A

ModelsB

A.1

Emotionality

B.1 Evaluation: Predictions vs. Original Data

Buechel et al. (2020) presented a new methodology to automatically generate lexicons for 91 languages comprising eight emotional variables: Valence, Arousal, Dominance (VAD) as well as the fivebasic emotions Joy, Anger, Surprise, Fear, Surprise(BE5) (Ekman, 1992). As a source dataset, theyused the English emotion lexicon from Warriner
et al. (2013), comprising about 14K entries in VAD
format collected via crowdsourcing. They applied
the BE5 ratings from Buechel and Hahn (2018a)
to convert the VAD ratings. Via their monolingualstate-of-the-art multi-task feed-forward network(Buechel and Hahn, 2018b), they projected ratings
on these eight variables, resulting in an English
lexicon containing 2M word type entries with very
high correlation with human judgments (around
90% for each variable).
We are interested in the emotionality of lexical
items, i.e., the emotional load that a term conveys,
rather than the actual emotion a term refers to. We
therefore use the BE5 ratings of the English lexicon
for our study. Out of the five scores that a term
receives – one for each emotion, we assume that its
highest score is reflective of the "emotional load"of that term, i.e., how much emotion it conveys.
For example, the lexical item "truth" obtained thescores 2.24, 1.46, 1.4, 1.49, 1.46 for Joy, Anger,
Sadness, Fear and Surprise, respectively. In our
experiments, the term "truth" is therefore attributed
the score of 2.24 as its emotional load.
A.2

Lexical Coherence Graph

Following Mesgar and Strube (2016), we measure the semantic relatedness between words represented by their word embeddings, computing thecosine score between the two words of the expression (SV or VO) with each word of the preceding
discourse. Consider va , vb , vc , the word vectors forword a in the preceding discourse A, word b in themetaphorical expression Bm contained in the lastsentence B and word c in the literal expression Cl
contained in the last sentence C, respectively. The
cosine scores cos(va , vb ) and cos(va , vc ) between
the two word vectors is a measure of semantic connectivity of the two words. The range of cos(va , vb )and cos(va , vc ) is between [−1, +1], showing how
well the two words are semantically correlated. Figure 4 shows how relatedness is measured, and figure 5 shows the output of the graph.

Results

Figure 3 presents the percentages of metaphoricalexpressions predicted by each model. As mentioned in section 4, the accuracy scores of all models reach around 50%, but they actually behave
very differently with regard to performances formetaphorical vs. literal predictions. Remember thatthe originally-collected discourses are perfectly balanced (cf. Section 2), where half of the discourses
contains metaphorical expressions and the otherhalf contains literal expressions from synonymous
pairs. As expected, the frequency baseline therefore reaches 50% of accuracy. We note howeverthat the literal usage of the pairs is the most frequent in the ukWaC corpus (37/50 pairs), whichleads the model to predict the literal counterpart
in 74% of the cases. The abstractness and emotionality models mostly select for the metaphorical usages (up to 100% for Emo.), as the discourses are considered more abstract and more
emotionally-loaded based on the respective norms.
This suggests that original speakers did not perceive the degree of abstractness and emotionalityof the discourse as triggers to favor one usage over
the other. This aligns with the findings in Piccirilli and Schulte im Walde (2022), who analyzed
the relationship between the abstractness and emotionality of the preceding discourses with humanpreferences for metaphorical vs. literal expressions.
The LCG model favors the literal expressions aswell (62%), while both BERTbase and BERTlarge predominantly predict the literal expressions (81.80%
and 80.10%, respectively.).

Figure 3: Percentages of metaphorical expressions predicted by each model.

Figure 4: Discourse A with three words {w1 , w2 , w3 } and sentences B and C with four words, where {w5 , w6 }are the two words composing the metaphorical expression, and {w9 , w10 } are composing the literal paraphrase.
Depending on the expression input (SV or VO), the respective subject or object is identical in {w5 , w6 } and{w9 , w10 }, as only the verb is used either as a metaphorical or a literal variant. The semantic relatedness between
each word in {w5 , w6 } and in {w9 , w10 } is computed with each word in A.

Figure 5: The word relation with the maximum weight (here Bm as indicated by the plain line) represents a stronger
connection with the preceding discourse A.

B.2

Evaluation: Predictions vs. Annotated Data

Figure 6 presents an overview of the proportion of expressions that are predicted metaphorically by themodels with regard to the preferences of the same expressions by human annotators.

Figure 6: Proportions of the metaphorical expressions predicted by the models with regard to the proportions ofthese usages to be favored by the participants.