FIGURE8: A Novel System for Generating and Evaluating
Figurative Language
Sarah Harmon
Computer Science Department
University of California, Santa Cruz
Santa Cruz, CA 95064 USA
smharmon@ucsc.edu
Abstract
Similes are easily obtained from web-driven and casebased
reasoning approaches. Still, generating thoughtful
figurative descriptions with meaningful relation to
narrative context and author style has not yet been fully
explored. In this paper, the author prepares the foundation
for a computational model which can achieve this
level of aesthetic complexity. This paper also introduces
and evaluates a possible architecture for generating and
ranking figurative comparisons on par with humans: the
FIGURE8 system.
Introduction
Figurative language is embedded within and intimately connected
to our cultures, behaviors, and models of the world.
In fact, humans use figurative language so often that we seldom
realize it (Lakoff and Johnson 1980); still, its utility
for communication is clear. Using metaphors and similes,
one can relate the unfamiliar, or the tenor, in terms of the
familiar, or vehicle (Richards 1980). In Figure 1, for example,
“moon” is the vehicle for “garden”, the tenor. Attributes
of the moon, such as its brilliance, are used to describe the
beauty of the garden. Prior to the comparison, the garden’s
appearance is unknown (is it beautiful and luminous, or neglected
and overgrown?). The simile helps to resolve this
ambiguity and provide the reader with a clearer picture of
the scene.
Comparison gives us the ability to delicately express irony
and sarcasm (“clear as mud”), exaggeration (“that man was
as tall as a giraffe”), and emotion (“my heart was a sinking
ship”). With such tools, we can explain how we feel,
what kinds of people we are, and what experiences we have
had. Further, metaphors give color to dry speech and are understood
faster than literal equivalents (Gibbs and Nagaoka
1985); this is likely due to their appeal to common previous
experiences and memories.
For the purpose of this paper, we will consider two styles
of figurative language: conventional (common analogies
used in daily language, such as “I see what you mean”) and
creative (original comparisons that call attention to themselves
as figures of speech, such as “Fear is a slinking cat I
find / Beneath the lilacs of my mind” (Tunnell 1977)). Each
type can provide value, although previous work on computational
generation of figurative language has primarily
focused on understanding and reconstructing conventional
metaphors and similes.
Cliches (e.g., “fast as lightning”) are arguably useful ´
when fast, informal communication is required between a
computer and a human, and such phrases can be learned via
web query (Veale and Hao 2007a). Generating creative comparisons
on par with human authors is a much more difficult
challenge. A conventional metaphor is considered “good” if
many others have used it before, but uniqueness and aesthetic
qualities are critical in generating a strong creative
metaphor. For instance, several aesthetic properties, such as
syllable counts, phonetics, stressed syllable position, rhyme,
and alliteration have been identified as “obvious” criteria for
making creative poetic lines sound good, despite the fact that
these “do not translate well into precise generative rules”
(Gervas, Herv ´ as, and Robinson 2007). While creative gen- ´
erators for figurative language exist, few address this concept
of what makes for a high-quality metaphor or simile. I
will describe a system, FIGURE8, which contains a novel
underlying model for what defines creative and high quality
figurative comparisons, and evaluates its own output based
on these rules.
Related Work
Modern research in creativity has generally defined a creative
system as one that generates novel, context-appropriate
output (Rothenberg and Hausman 1976; Sawyer 2012).
Within the context of creative natural language generation, a
third criterion has been noted: a creative system must generate
context-appropriate knowledge outside of its pre-existing
knowledge base (Perez y P ´ erez and Sharples 2004). ´
Several computational systems exist which attempt to
meet this benchmark. ASPERA, for instance, combines
case-based reasoning with intelligent adaptation of examples
from corpora (Gervas 2000). Psychological theories ´
have further informed the art of generating figurative language,
resulting in more advanced and thoughtful systems.
Notably, Brown (Ortony 1993) and Glucksberg (Glucksberg
2001) have argued that categorization is inherent to
metaphor. As a consequence, the concept of propertybased
concept mapping has inspired metaphor generation
approaches, and has been cited as the best method for producing
robust, scalable and useful metaphors (Hervas et al. ´
2007; Veale and Hao 2007a).
Proceedings of the Sixth International Conference on Computational Creativity June 2015 71
One must also consider how to develop an appropriate
knowledge base without substantial manual authoring. Previous
exemplary work in metaphor generation has emphasized
the power of using the web to establish example cases
of valid comparisons (Veale and Hao 2007a; 2007b). However,
these systems merely generate large amounts of potentially
creative descriptions, and cannot distinguish between
original and poor quality comparisons (Veale and Hao
2007a). Further, they often ignore context, sentence construction,
and aesthetics in the generation process, resulting
in less evocative and meaningful language.
FIGURE8 is a system that uses a web-driven approach
to form a preliminary knowledge base of nouns and their
properties. The system is provided with a model of the current
world and an entity in the world to be described. A
suitable vehicle is selected from the knowledge base, and
the comparison between the two nouns is clarified by obtaining
an understanding via corpora search of what these
nouns can do and how they can be described. Sentence
completion occurs by intelligent adaptation of a case library
of valid grammar constructions. Finally, the comparison
is ranked by the system based on semantic, prosodic, and
knowledge-based qualities. In this way, FIGURE8 simulates
the human-authoring process of revision by generating
many vehicle choices and linguistic variations for a single
tenor, and choosing the best among them as its favorite.
While FIGURE8 does not claim to have a comprehensive set
of rules - for example, it does not consider phonetics in its
evaluation of description quality - it provides a novel foundation
for an intelligent figurative language generation and
assessment system.
Approach
Prior work has established that a strong creative metaphor is
not only comprehensible (Tourangeau 1981), novel (Camac
and Glucksberg 1984), and context-appropriate (Harwood
and Verbrugge 1977; Tversky 1977; Gildea and Glucksberg
1983), but surprising (Tourangeau 1981). The following sections
will illustrate how FIGURE8 considers these properties
when generating metaphors and similes. A block diagram
of the generation process is shown in Figure 3.
Clarity
A strong metaphor must have an understandable, accurate
link between tenor and vehicle. A vehicle is thus only
considered acceptable if it has properties in common with
the tenor. Further, associating the tenor with the capacities
and known manifestations of the vehicle should enhance the
clarity of the description. In the FIGURE8 system, these
associations are found by mining existing literary corpora
(Hart 2014) for instances of the vehicle and using NLTK’s
parts-of-speech tagging to identify associations (e.g., refer to
Figure 2). This procedure enables the system to use words
commonly associated with the vehicle to develop a fresh relation
to the tenor. For example, if we were to compare a
teacher to a horse, FIGURE8 may now be able to reason that
the teacher would prance or trot into the room. In this way,
a sentence can be generated by only implicitly referring to
the vehicle (“The teacher pranced into the room” vs. “The
teacher was a wild horse, prancing into the room”). Common
verbs, such as forms of “to be”, were culled from the
generated list of association because - as all nouns have the
capability to exist and be - such verbs do not lend clarity to
the comparison.
Granted, the word chosen to relate to the tenor may not
make sense (especially in the case of verbs), destroying the
very clarity it was meant to enhance. FIGURE8 thus performs
a web query using Python’s urllib module to ensure
that others have associated the chosen word with the tenor
before. If a previous association has not been made, the
metaphor is ranked lower in terms of estimated clarity. This
evaluation measure ensures that nonsensical descriptions,
such as “The turtle darkened like a blue ocean”, are given
a lower ranking overall.
Novelty
Cliches are frowned upon by expert authors; as Salvador ´
Dal´ı once said, “The first man to compare the cheeks of
a young woman to a rose was obviously a poet; the first
to repeat it was possibly an idiot” (1968). For computergenerated
text, it is thus reasonable to expect that a quality
metaphor is a fresh comparison. In the FIGURE8 system,
each metaphor is checked against an existing knowledge
base of comparisons (Friedman 1996), and all generations
are ranked based on their similarity to conventional
metaphors in this database.
Aptness
Ideally, a strong metaphor will fit the context within which
it lives. For usage in a narrative context, the FIGURE8 system
can be passed a model of a simple world of objects and
character models, and incorporate these appropriately into
its eventual output along with a prepositional phrase generation
module. Additionally, one may ask FIGURE8 to
generate ironic comparisons, such as those generated by a
sarcastic character when speaking. Irony is achieved by selecting
for properties with the exact opposite meanings, in
accordance with prior work (Veale and Hao 2007b). The
FIGURE8 system also endeavors to match a given context
during sentence completion, which will be described in a
later section.
Unpredictability
Metaphors are perceived as cleverer when the vehicle and
tenor contain similarities, but the respective domains of
these terms are distinct (Tourangeau 1981). A description
is thus ranked as more surprising when the words are not
very conceptually similar and contain fewer properties in
common. With the assumption that they share at least one
property in common, the chosen metaphor components are
ranked by querying the UMBC Semantic Similarity System
(Han et al. 2013). The degree to which the vehicle
and tenor share major categories is also considered by using
a function similar to WordNet’s lexname query. This
check is needed because if one or more major categories are
shared, the metaphor is considerably less surprising. For
Proceedings of the Sixth International Conference on Computational Creativity June 2015 72
Figure 1: Example of a highly ranked output sentence by FIGURE8. Here, the tenor, vehicle, and associated phrases are garden,
moon, lit up, and pale. The nouns garden and moon not only have low semantic similarity, but do not share a major category
together. Likening a garden to a moon is also not a cliche comparison, lending to the description’s potential novelty. ´
Figure 2: Example of how FIGURE8 discovers and associates a verb with a chosen vehicle, using text from The Count of Monte
Cristo, and a part-of-speech parsing module similar to the Stanford Parser (Socher et al. 2013). Here, the nsubj label refers
to a link between a verb (“deceived”) and a noun phrase (in this case, the vehicle “world”). The remaining labels in the figure
represent the part-of-speech tags.
instance, “the strawberry is a pomegranate” is considered
a poor metaphor because strawberry and pomegranate are
contained within a major category: fruits. Such a description
may be produced by a web-based generator (for instance,
the online MIT-licensed Metaphorgy system (Groff-Palermo
and Lawson 2013) produces “My strawberry is a Phaeacian
cherry”), but will be given a low ranking by FIGURE8.
Prosody
The prosody of a metaphor can be defined as the rhythmic,
tonal, and aesthetic qualities that distinguish one metaphor
from another. Descriptions are ranked highly if their
prosody is of consistent and high quality. For instance, consider
the following similes:
(1) The serpent stretched into the horizon, like a deserted
desert.
(2) The snake extended into the horizon, like an abandoned
desert.
Although alliteration and assonance can be used beautifully
in figurative language, the high similarity of consecutive
words in (1) may be distracting. Example (2) depicts the
same imagery, but uses words of greater distance in terms of
consecutive string similarity.
At present, FIGURE8 conducts string similarity via
Python’s difflib to evaluate the prosody of its outputs. Using
difflib’s SequenceMatcher, one can determine a value
indicating the degree of similarity between two input strings
in a range from 0 (no similarity) to 1 (identical strings).
FIGURE8 is thus able to quantify the string similarity for
consecutive words, and ranks descriptions lower if there are
many consecutive string similarity values above 0.7, which
was deemed an appropriate threshold by the author. Consecutive
words are also checked for alliteration and assonance,
which are considered positive qualities by FIGURE8.
Sentence Completion
Automated metaphor identification in text has been thoroughly
explored (Neuman et al. 2013; Steen et al. 2010)
and, as such, FIGURE8 has been provided with a case library
of appropriate sentence constructions for metaphor
and simile. By following the procedure of imaginative recall
(Turner 1992), FIGURE8 first attempts to fit the provided
context of the situation to an exact, pre-existing solution.
If no solution exists, FIGURE8 searches its memory, solves
the problem for a similar case, and adapts that solution to the
provided context. As an illustration: if FIGURE8 notes that
other authors have used the phrase “to the barn”, it should
recognize the barn as a noun denoting a man-made object
via WordNet. Similarly, a “chair” is a man-made object, and
thus, FIGURE8 may decide to replace “barn” with “chair”
when told that a chair exists in the current narrative context.
This adaptive process enables FIGURE8 to match its constructions
to any provided context and complete statements
creatively.
Evaluation
Little research, if any, has worked towards developing a
model of what makes a high quality computer-generated
metaphor. Although there is no standard method to evaluate
computationally-generated figurative descriptions, one reasonable
way to judge would seem to be agreement with huProceedings
of the Sixth International Conference on Computational Creativity June 2015 73
Figure 3: Block diagram of the FIGURE8 generation system. If no world model is given, a tenor is selected at random from the
noun-property database. A vehicle is then selected with at least one property in common with the tenor. The Clarity Enhancer
module requests verbs and adjectives associated with the vehicle from mined literary corpora. Finally, the sentence is completed
by performing imaginative recall with known valid sentence constructions for metaphor identified from literary corpora.
Table 1: Comparison 1 of FIGURE8 and human rankings for clarity and overall quality. In this set, FIGURE8 was asked to
generate and rank figurative descriptions given “pearl” as the tenor. Human clarity and likability rankings were found to be
highly correlated (⇢ = 0.684). Spearman analyses also indicated positive correlations between human and FIGURE8 rankings
(clarity: ⇢ = 0.872; quality: ⇢ = 0.821).
man ratings. This can be assessed by requesting humans to
rank descriptions generated by the FIGURE8 algorithm, and
determining if the majority are in agreement with the computer’s
(FIGURE8’s) ranking. A pilot study indicated that
providing each description with additional context would
make the ranking process too time-consuming for participants.
Thus, functions to enhance aptness were not included
when generating outputs to be evaluated in the full-scale
study.
Method
One hundred participants (73 female, 27 male) were recruited
via Amazon’s Mechanical Turk. Each participant
viewed a series of five sentences at a time, and were asked to
rank the similes by how understandable they were (clarity),
and by how much they, as individuals, enjoyed the comparison
(likability). Each set of five sentences contained the
same tenor, and were originally generated and ranked by
FIGURE8. The sets were not hand-selected by the author.
That is, the first eleven sets FIGURE8 generated and ranked
were used in the study.
Results
Human preferences were determined by following the majority
criterion. As seen in Figures 4 and 5, human clarity
ratings were often positively correlated with overall quality
ratings, and this correlation was confirmed with Spearman
analyses. Overall, FIGURE8’s top result for clarity and
overall quality generally agreed with the human rankings for
each of the eleven sets. FIGURE8 exactly matched the first
ranking 46% of the time for clarity and likability. Further,
it matched either the first or second ranking 82% and 100%
of the time for the clarity and likability categories, respectively.
Examples of how FIGURE8 matched human ratings
are shown in Tables 1, 2, and 3.
Discussion and Future Work
In this paper, the author has introduced the FIGURE8 system
as a novel tool for generating and evaluating creative figurative
descriptions. FIGURE8’s assessments are grounded
in psychological models of metaphor comprehension, and
have thus far been found to adequately match human rankings
when agreed upon.
Participants in the evaluation portion were not told that
the descriptions were generated by a computer. Only two
Proceedings of the Sixth International Conference on Computational Creativity June 2015 74
Table 2: Comparison 2 of FIGURE8 and human rankings for sentences of tenor “snow”. Human clarity and likability rankings
were found to be positively correlated (⇢ = 0.763). Spearman correlation analysis suggested that FIGURE8 clarity rankings
were positively associated with human clarity rankings (⇢ = 0.872), but no significant association was found between likability
rankings in this case (⇢ = -0.359).
Table 3: A third comparison of FIGURE8 and human rankings for sentences of tenor “queen”. In addition to showing first
choice rankings, this table displays human rankings when considering first and second choices. That is, “the queen stands like
a strong castle” was ranked as either first or second for the majority of respondents. In both cases, human clarity and likability
rankings were found to be positively correlated (⇢ > 0.9). Spearman analyses also suggested for both cases that FIGURE8 and
human rankings for clarity and likability were positively correlated with high significance (⇢ > 0.7).
Figure 4: First choice rankings for the generated set of sentences
using pearl as the tenor. Although some disparities
existed, the majority of respondents generally agreed upon
which sentence was the most understandable.
comments were made about checking sentences for validity
prior to including them in the study, and one regarding how
painful it was to rank “bad poetry”. Most participants, however,
enjoyed the task and provided positive feedback about
their experience (“cool hit”,“super fun”,“I love this”). It is
conceivable that task enjoyment affected user responses, but
controlling for explicit indication of task enjoyment yielded
no significant difference in the results. Controlling for gender
also did not reveal significantly different outcomes.
Interestingly, for roughly half (50-60% per set) of the
participants, how much they liked the figurative description
was directly correlated with how well they understood it.
The most highly ranked phrases for clarity were also often
ranked first for likability, and the Spearman coefficient
was used to confirm these positive associations. This was a
surprising finding, because more variation and subjectivity
was expected for these ratings. Discrepancies between human
and FIGURE8 likability rankings, such as in Table 2,
could potentially be explained by a human tendency to prefer
metaphors containing words of positive sentiment value.
However, more analysis is required to confirm this idea, and
further study is needed to evaluate how qualities of language
are weighted across general and expert populations. Judging
from participant comments, it is also possible that some people
may like metaphors primarily based on qualities other
than clarity (such as prosody, sentiment, or whimsy). If
these groups could be automatically identified, perhaps future
computer-produced descriptions could adapt to generate
more personalized descriptions for the optimum enjoyment
of the reader.
While FIGURE8 is able to rank its figurative descriptions
over various measures of quality, how well its output comProceedings
of the Sixth International Conference on Computational Creativity June 2015 75
Figure 5: Clarity rankings for the generated set of sentences
using snow as the tenor. Participants rated what FIGURE8
considered the most unsurprising metaphor as the
most clear, but there was no highly significant consensus regarding
the most likable description.
pares with human-authored descriptions was not assessed.
The fact that most participants in the evaluation did not question
the source of the texts is a promising sign that the system
presented here generates human-like output. Regardless, its
present constructions can be automatically assigned rankings
on par with human evaluations. It is assumed that as the
quality of FIGURE8’s generations increases, it will be able
to extract the best output from the results of its “brainstorming”.
Future research should build upon this foundation and
work towards evaluating computer-generated descriptions in
terms of aptness, prosody, and unpredictability. When machines
are fully able to grasp the subtleties and aesthetics of
figurative language, we as humans will be able to relate to
them as never before.
<references_biblio/>
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Proceedings of the Sixth International Conference on Computational Creativity June 2015 77FIGURE8: 
A 
Novel 
System 
for 
Generating 
and 
Evaluating
Figurative 
Language 


Sarah 
Harmon 


Computer 
Science 
Department 
University 
of 
California, 
Santa 
Cruz 
Santa 
Cruz, 
CA 
95064 
USA 
smharmon@ucsc.edu 


Abstract 


Similes 
are 
easily 
obtained 
from 
web-driven 
and 
case-
based 
reasoning 
approaches. 
Still, 
generating 
thoughtful 
figurative 
descriptions 
with 
meaningful 
relation 
to 
narrative 
context 
and 
author 
style 
has 
not 
yet 
been 
fully 
explored. 
In 
this 
paper, 
the 
author 
prepares 
the 
foundation 
for 
a 
computational 
model 
which 
can 
achieve 
this 
level 
of 
aesthetic 
complexity. 
This 
paper 
also 
introduces 
and 
evaluates 
a 
possible 
architecture 
for 
generating 
and 
ranking 
figurative 
comparisons 
on 
par 
with 
humans: 
the 
FIGURE8 
system. 


Introduction 


Figurative 
language 
is 
embedded 
within 
and 
intimately 
connected 
to 
our 
cultures, 
behaviors, 
and 
models 
of 
the 
world. 
In 
fact, 
humans 
use 
figurative 
language 
so 
often 
that 
we 
seldom 
realize 
it 
(Lakoff 
and 
Johnson 
1980); 
still, 
its 
utility 
for 
communication 
is 
clear. 
Using 
metaphors 
and 
similes, 
one 
can 
relate 
the 
unfamiliar, 
or 
the 
tenor, 
in 
terms 
of 
the 
familiar, 
or 
vehicle 
(Richards 
1980). 
In 
Figure 
1, 
for 
example, 
“moon” 
is 
the 
vehicle 
for 
“garden”, 
the 
tenor. 
Attributes 
of 
the 
moon, 
such 
as 
its 
brilliance, 
are 
used 
to 
describe 
the 
beauty 
of 
the 
garden. 
Prior 
to 
the 
comparison, 
the 
garden’s 
appearance 
is 
unknown 
(is 
it 
beautiful 
and 
luminous, 
or 
neglected 
and 
overgrown?). 
The 
simile 
helps 
to 
resolve 
this 
ambiguity 
and 
provide 
the 
reader 
with 
a 
clearer 
picture 
of 
the 
scene. 


Comparison 
gives 
us 
the 
ability 
to 
delicately 
express 
irony 
and 
sarcasm 
(“clear 
as 
mud”), 
exaggeration 
(“that 
man 
was 
as 
tall 
as 
a 
giraffe”), 
and 
emotion 
(“my 
heart 
was 
a 
sinking 
ship”). 
With 
such 
tools, 
we 
can 
explain 
how 
we 
feel, 
what 
kinds 
of 
people 
we 
are, 
and 
what 
experiences 
we 
have 
had. 
Further, 
metaphors 
give 
color 
to 
dry 
speech 
and 
are 
understood 
faster 
than 
literal 
equivalents 
(Gibbs 
and 
Nagaoka 
1985); 
this 
is 
likely 
due 
to 
their 
appeal 
to 
common 
previous 
experiences 
and 
memories. 


For 
the 
purpose 
of 
this 
paper, 
we 
will 
consider 
two 
styles 
of 
figurative 
language: 
conventional 
(common 
analogies 
used 
in 
daily 
language, 
such 
as 
“I 
see 
what 
you 
mean”) 
and 
creative 
(original 
comparisons 
that 
call 
attention 
to 
themselves 
as 
figures 
of 
speech, 
such 
as 
“Fear 
is 
a 
slinking 
cat 
I 
find 
/ 
Beneath 
the 
lilacs 
of 
my 
mind” 
(Tunnell 
1977)). 
Each 
type 
can 
provide 
value, 
although 
previous 
work 
on 
computational 
generation 
of 
figurative 
language 
has 
primarily 


focused 
on 
understanding 
and 
reconstructing 
conventional 
metaphors 
and 
similes. 


Clich´es 
(e.g., 
“fast 
as 
lightning”) 
are 
arguably 
useful 
when 
fast, 
informal 
communication 
is 
required 
between 
a 
computer 
and 
a 
human, 
and 
such 
phrases 
can 
be 
learned 
via 
web 
query 
(Veale 
and 
Hao 
2007a). 
Generating 
creative 
comparisons 
on 
par 
with 
human 
authors 
is 
a 
much 
more 
difficult 
challenge. 
A 
conventional 
metaphor 
is 
considered 
“good” 
if 
many 
others 
have 
used 
it 
before, 
but 
uniqueness 
and 
aesthetic 
qualities 
are 
critical 
in 
generating 
a 
strong 
creative 
metaphor. 
For 
instance, 
several 
aesthetic 
properties, 
such 
as 
syllable 
counts, 
phonetics, 
stressed 
syllable 
position, 
rhyme, 
and 
alliteration 
have 
been 
identified 
as 
“obvious” 
criteria 
for 
making 
creative 
poetic 
lines 
sound 
good, 
despite 
the 
fact 
that 
these 
“do 
not 
translate 
well 
into 
precise 
generative 
rules” 
(Gerv´as, 
and 
Robinson 
2007). 
While 
creative 
gen-

as, 
Herv´
erators 
for 
figurative 
language 
exist, 
few 
address 
this 
concept 
of 
what 
makes 
for 
a 
high-quality 
metaphor 
or 
simile. 
I 
will 
describe 
a 
system, 
FIGURE8, 
which 
contains 
a 
novel 
underlying 
model 
for 
what 
defines 
creative 
and 
high 
quality 
figurative 
comparisons, 
and 
evaluates 
its 
own 
output 
based 
on 
these 
rules. 


Related 
Work 


Modern 
research 
in 
creativity 
has 
generally 
defined 
a 
creative 
system 
as 
one 
that 
generates 
novel, 
context-appropriate 
output 
(Rothenberg 
and 
Hausman 
1976; 
Sawyer 
2012). 
Within 
the 
context 
of 
creative 
natural 
language 
generation, 
a 
third 
criterion 
has 
been 
noted: 
a 
creative 
system 
must 
generate 
context-appropriate 
knowledge 
outside 
of 
its 
pre-existing 
knowledge 
base 
(P´erez 
and 
Sharples 
2004). 


erez 
y 
P´

Several 
computational 
systems 
exist 
which 
attempt 
to 
meet 
this 
benchmark. 
ASPERA, 
for 
instance, 
combines 
case-based 
reasoning 
with 
intelligent 
adaptation 
of 
examples 
from 
corpora 
(Gerv´as 
2000). 
Psychological 
theories 
have 
further 
informed 
the 
art 
of 
generating 
figurative 
language, 
resulting 
in 
more 
advanced 
and 
thoughtful 
systems. 
Notably, 
Brown 
(Ortony 
1993) 
and 
Glucksberg 
(Glucksberg 
2001) 
have 
argued 
that 
categorization 
is 
inherent 
to 
metaphor. 
As 
a 
consequence, 
the 
concept 
of 
property-
based 
concept 
mapping 
has 
inspired 
metaphor 
generation 
approaches, 
and 
has 
been 
cited 
as 
the 
best 
method 
for 
producing 
robust, 
scalable 
and 
useful 
metaphors 
(Herv´as 
et 
al. 
2007; 
Veale 
and 
Hao 
2007a). 


Proceedings of the Sixth International Conference on Computational Creativity June 2015 


One 
must 
also 
consider 
how 
to 
develop 
an 
appropriate 
knowledge 
base 
without 
substantial 
manual 
authoring. 
Previous 
exemplary 
work 
in 
metaphor 
generation 
has 
emphasized 
the 
power 
of 
using 
the 
web 
to 
establish 
example 
cases 
of 
valid 
comparisons 
(Veale 
and 
Hao 
2007a; 
2007b). 
However, 
these 
systems 
merely 
generate 
large 
amounts 
of 
potentially 
creative 
descriptions, 
and 
cannot 
distinguish 
between 
original 
and 
poor 
quality 
comparisons 
(Veale 
and 
Hao 
2007a). 
Further, 
they 
often 
ignore 
context, 
sentence 
construction, 
and 
aesthetics 
in 
the 
generation 
process, 
resulting 
in 
less 
evocative 
and 
meaningful 
language. 


FIGURE8 
is 
a 
system 
that 
uses 
a 
web-driven 
approach 
to 
form 
a 
preliminary 
knowledge 
base 
of 
nouns 
and 
their 
properties. 
The 
system 
is 
provided 
with 
a 
model 
of 
the 
current 
world 
and 
an 
entity 
in 
the 
world 
to 
be 
described. 
A 
suitable 
vehicle 
is 
selected 
from 
the 
knowledge 
base, 
and 
the 
comparison 
between 
the 
two 
nouns 
is 
clarified 
by 
obtaining 
an 
understanding 
via 
corpora 
search 
of 
what 
these 
nouns 
can 
do 
and 
how 
they 
can 
be 
described. 
Sentence 
completion 
occurs 
by 
intelligent 
adaptation 
of 
a 
case 
library 
of 
valid 
grammar 
constructions. 
Finally, 
the 
comparison 
is 
ranked 
by 
the 
system 
based 
on 
semantic, 
prosodic, 
and 
knowledge-based 
qualities. 
In 
this 
way, 
FIGURE8 
simulates 
the 
human-authoring 
process 
of 
revision 
by 
generating 
many 
vehicle 
choices 
and 
linguistic 
variations 
for 
a 
single 
tenor, 
and 
choosing 
the 
best 
among 
them 
as 
its 
favorite. 
While 
FIGURE8 
does 
not 
claim 
to 
have 
a 
comprehensive 
set 
of 
rules 
-for 
example, 
it 
does 
not 
consider 
phonetics 
in 
its 
evaluation 
of 
description 
quality 
-it 
provides 
a 
novel 
foundation 
for 
an 
intelligent 
figurative 
language 
generation 
and 
assessment 
system. 


Approach 


Prior 
work 
has 
established 
that 
a 
strong 
creative 
metaphor 
is 
not 
only 
comprehensible 
(Tourangeau 
1981), 
novel 
(Camac 
and 
Glucksberg 
1984), 
and 
context-appropriate 
(Harwood 
and 
Verbrugge 
1977; 
Tversky 
1977; 
Gildea 
and 
Glucksberg 
1983), 
but 
surprising 
(Tourangeau 
1981). 
The 
following 
sections 
will 
illustrate 
how 
FIGURE8 
considers 
these 
properties 
when 
generating 
metaphors 
and 
similes. 
A 
block 
diagram 
of 
the 
generation 
process 
is 
shown 
in 
Figure 
3. 


Clarity 


A 
strong 
metaphor 
must 
have 
an 
understandable, 
accurate 
link 
between 
tenor 
and 
vehicle. 
A 
vehicle 
is 
thus 
only 
considered 
acceptable 
if 
it 
has 
properties 
in 
common 
with 
the 
tenor. 
Further, 
associating 
the 
tenor 
with 
the 
capacities 
and 
known 
manifestations 
of 
the 
vehicle 
should 
enhance 
the 
clarity 
of 
the 
description. 
In 
the 
FIGURE8 
system, 
these 
associations 
are 
found 
by 
mining 
existing 
literary 
corpora 
(Hart 
2014) 
for 
instances 
of 
the 
vehicle 
and 
using 
NLTK’s 
parts-of-speech 
tagging 
to 
identify 
associations 
(e.g., 
refer 
to 
Figure 
2). 
This 
procedure 
enables 
the 
system 
to 
use 
words 
commonly 
associated 
with 
the 
vehicle 
to 
develop 
a 
fresh 
relation 
to 
the 
tenor. 
For 
example, 
if 
we 
were 
to 
compare 
a 
teacher 
to 
a 
horse, 
FIGURE8 
may 
now 
be 
able 
to 
reason 
that 
the 
teacher 
would 
prance 
or 
trot 
into 
the 
room. 
In 
this 
way, 
a 
sentence 
can 
be 
generated 
by 
only 
implicitly 
referring 
to 


the 
vehicle 
(“The 
teacher 
pranced 
into 
the 
room” 
vs. 
“The 
teacher 
was 
a 
wild 
horse, 
prancing 
into 
the 
room”). 
Common 
verbs, 
such 
as 
forms 
of 
“to 
be”, 
were 
culled 
from 
the 
generated 
list 
of 
association 
because 
-as 
all 
nouns 
have 
the 
capability 
to 
exist 
and 
be 
-such 
verbs 
do 
not 
lend 
clarity 
to 
the 
comparison. 


Granted, 
the 
word 
chosen 
to 
relate 
to 
the 
tenor 
may 
not 
make 
sense 
(especially 
in 
the 
case 
of 
verbs), 
destroying 
the 
very 
clarity 
it 
was 
meant 
to 
enhance. 
FIGURE8 
thus 
performs 
a 
web 
query 
using 
Python’s 
urllib 
module 
to 
ensure 
that 
others 
have 
associated 
the 
chosen 
word 
with 
the 
tenor 
before. 
If 
a 
previous 
association 
has 
not 
been 
made, 
the 
metaphor 
is 
ranked 
lower 
in 
terms 
of 
estimated 
clarity. 
This 
evaluation 
measure 
ensures 
that 
nonsensical 
descriptions, 
such 
as 
“The 
turtle 
darkened 
like 
a 
blue 
ocean”, 
are 
given 
a 
lower 
ranking 
overall. 


Novelty 


Clich´

es 
are 
frowned 
upon 
by 
expert 
authors; 
as 
Salvador 
Dal´i 
once 
said, 
“The 
first 
man 
to 
compare 
the 
cheeks 
of 
a 
young 
woman 
to 
a 
rose 
was 
obviously 
a 
poet; 
the 
first 
to 
repeat 
it 
was 
possibly 
an 
idiot” 
(1968). 
For 
computer-
generated 
text, 
it 
is 
thus 
reasonable 
to 
expect 
that 
a 
quality 
metaphor 
is 
a 
fresh 
comparison. 
In 
the 
FIGURE8 
system, 
each 
metaphor 
is 
checked 
against 
an 
existing 
knowledge 
base 
of 
comparisons 
(Friedman 
1996), 
and 
all 
generations 
are 
ranked 
based 
on 
their 
similarity 
to 
conventional 
metaphors 
in 
this 
database. 


Aptness 


Ideally, 
a 
strong 
metaphor 
will 
fit 
the 
context 
within 
which 
it 
lives. 
For 
usage 
in 
a 
narrative 
context, 
the 
FIGURE8 
system 
can 
be 
passed 
a 
model 
of 
a 
simple 
world 
of 
objects 
and 
character 
models, 
and 
incorporate 
these 
appropriately 
into 
its 
eventual 
output 
along 
with 
a 
prepositional 
phrase 
generation 
module. 
Additionally, 
one 
may 
ask 
FIGURE8 
to 
generate 
ironic 
comparisons, 
such 
as 
those 
generated 
by 
a 
sarcastic 
character 
when 
speaking. 
Irony 
is 
achieved 
by 
selecting 
for 
properties 
with 
the 
exact 
opposite 
meanings, 
in 
accordance 
with 
prior 
work 
(Veale 
and 
Hao 
2007b). 
The 
FIGURE8 
system 
also 
endeavors 
to 
match 
a 
given 
context 
during 
sentence 
completion, 
which 
will 
be 
described 
in 
a 
later 
section. 


Unpredictability 


Metaphors 
are 
perceived 
as 
cleverer 
when 
the 
vehicle 
and 
tenor 
contain 
similarities, 
but 
the 
respective 
domains 
of 
these 
terms 
are 
distinct 
(Tourangeau 
1981). 
A 
description 
is 
thus 
ranked 
as 
more 
surprising 
when 
the 
words 
are 
not 
very 
conceptually 
similar 
and 
contain 
fewer 
properties 
in 
common. 
With 
the 
assumption 
that 
they 
share 
at 
least 
one 
property 
in 
common, 
the 
chosen 
metaphor 
components 
are 
ranked 
by 
querying 
the 
UMBC 
Semantic 
Similarity 
System 
(Han 
et 
al. 
2013). 
The 
degree 
to 
which 
the 
vehicle 
and 
tenor 
share 
major 
categories 
is 
also 
considered 
by 
using 
a 
function 
similar 
to 
WordNet’s 
lexname 
query. 
This 
check 
is 
needed 
because 
if 
one 
or 
more 
major 
categories 
are 
shared, 
the 
metaphor 
is 
considerably 
less 
surprising. 
For 


Proceedings of the Sixth International Conference on Computational Creativity June 2015 


Figure 
1: 
Example 
of 
a 
highly 
ranked 
output 
sentence 
by 
FIGURE8. 
Here, 
the 
tenor, 
vehicle, 
and 
associated 
phrases 
are 
garden, 
moon, 
lit 
up, 
and 
pale. 
The 
nouns 
garden 
and 
moon 
not 
only 
have 
low 
semantic 
similarity, 
but 
do 
not 
share 
a 
major 
category 
together. 
Likening 
a 
garden 
to 
a 
moon 
is 
also 
not 
a 
clich´e 
comparison, 
lending 
to 
the 
description’s 
potential 
novelty. 



Figure 
2: 
Example 
of 
how 
FIGURE8 
discovers 
and 
associates 
a 
verb 
with 
a 
chosen 
vehicle, 
using 
text 
from 
The 
Count 
of 
Monte 
Cristo, 
and 
a 
part-of-speech 
parsing 
module 
similar 
to 
the 
Stanford 
Parser 
(Socher 
et 
al. 
2013). 
Here, 
the 
nsubj 
label 
refers 
to 
a 
link 
between 
a 
verb 
(“deceived”) 
and 
a 
noun 
phrase 
(in 
this 
case, 
the 
vehicle 
“world”). 
The 
remaining 
labels 
in 
the 
figure 
represent 
the 
part-of-speech 
tags. 


instance, 
“the 
strawberry 
is 
a 
pomegranate” 
is 
considered 
a 
poor 
metaphor 
because 
strawberry 
and 
pomegranate 
are 
contained 
within 
a 
major 
category: 
fruits. 
Such 
a 
description 
may 
be 
produced 
by 
a 
web-based 
generator 
(for 
instance, 
the 
online 
MIT-licensed 
Metaphorgy 
system 
(Groff-Palermo 
and 
Lawson 
2013) 
produces 
“My 
strawberry 
is 
a 
Phaeacian 
cherry”), 
but 
will 
be 
given 
a 
low 
ranking 
by 
FIGURE8. 


Prosody 


The 
prosody 
of 
a 
metaphor 
can 
be 
defined 
as 
the 
rhythmic, 
tonal, 
and 
aesthetic 
qualities 
that 
distinguish 
one 
metaphor 
from 
another. 
Descriptions 
are 
ranked 
highly 
if 
their 
prosody 
is 
of 
consistent 
and 
high 
quality. 
For 
instance, 
consider 
the 
following 
similes: 


(1) 
The 
serpent 
stretched 
into 
the 
horizon, 
like 
a 
deserted 
desert. 
(2) 
The 
snake 
extended 
into 
the 
horizon, 
like 
an 
abandoned 
desert. 
Although 
alliteration 
and 
assonance 
can 
be 
used 
beautifully 
in 
figurative 
language, 
the 
high 
similarity 
of 
consecutive 
words 
in 
(1) 
may 
be 
distracting. 
Example 
(2) 
depicts 
the 
same 
imagery, 
but 
uses 
words 
of 
greater 
distance 
in 
terms 
of 
consecutive 
string 
similarity. 


At 
present, 
FIGURE8 
conducts 
string 
similarity 
via 
Python’s 
difflib 
to 
evaluate 
the 
prosody 
of 
its 
outputs. 
Using 
difflib’s 
SequenceMatcher, 
one 
can 
determine 
a 
value 
indicating 
the 
degree 
of 
similarity 
between 
two 
input 
strings 
in 
a 
range 
from 
0 
(no 
similarity) 
to 
1 
(identical 
strings). 
FIGURE8 
is 
thus 
able 
to 
quantify 
the 
string 
similarity 
for 
consecutive 
words, 
and 
ranks 
descriptions 
lower 
if 
there 
are 


many 
consecutive 
string 
similarity 
values 
above 
0.7, 
which 
was 
deemed 
an 
appropriate 
threshold 
by 
the 
author. 
Consecutive 
words 
are 
also 
checked 
for 
alliteration 
and 
assonance, 
which 
are 
considered 
positive 
qualities 
by 
FIGURE8. 


Sentence 
Completion 


Automated 
metaphor 
identification 
in 
text 
has 
been 
thoroughly 
explored 
(Neuman 
et 
al. 
2013; 
Steen 
et 
al. 
2010) 
and, 
as 
such, 
FIGURE8 
has 
been 
provided 
with 
a 
case 
library 
of 
appropriate 
sentence 
constructions 
for 
metaphor 
and 
simile. 
By 
following 
the 
procedure 
of 
imaginative 
recall 
(Turner 
1992), 
FIGURE8 
first 
attempts 
to 
fit 
the 
provided 
context 
of 
the 
situation 
to 
an 
exact, 
pre-existing 
solution. 
If 
no 
solution 
exists, 
FIGURE8 
searches 
its 
memory, 
solves 
the 
problem 
for 
a 
similar 
case, 
and 
adapts 
that 
solution 
to 
the 
provided 
context. 
As 
an 
illustration: 
if 
FIGURE8 
notes 
that 
other 
authors 
have 
used 
the 
phrase 
“to 
the 
barn”, 
it 
should 
recognize 
the 
barn 
as 
a 
noun 
denoting 
a 
man-made 
object 
via 
WordNet. 
Similarly, 
a 
“chair” 
is 
a 
man-made 
object, 
and 
thus, 
FIGURE8 
may 
decide 
to 
replace 
“barn” 
with 
“chair” 
when 
told 
that 
a 
chair 
exists 
in 
the 
current 
narrative 
context. 
This 
adaptive 
process 
enables 
FIGURE8 
to 
match 
its 
constructions 
to 
any 
provided 
context 
and 
complete 
statements 
creatively. 


Evaluation 


Little 
research, 
if 
any, 
has 
worked 
towards 
developing 
a 
model 
of 
what 
makes 
a 
high 
quality 
computer-generated 
metaphor. 
Although 
there 
is 
no 
standard 
method 
to 
evaluate 
computationally-generated 
figurative 
descriptions, 
one 
reasonable 
way 
to 
judge 
would 
seem 
to 
be 
agreement 
with 
hu-

Proceedings of the Sixth International Conference on Computational Creativity June 2015 


Figure 
3: 
Block 
diagram 
of 
the 
FIGURE8 
generation 
system. 
If 
no 
world 
model 
is 
given, 
a 
tenor 
is 
selected 
at 
random 
from 
the 
noun-property 
database. 
A 
vehicle 
is 
then 
selected 
with 
at 
least 
one 
property 
in 
common 
with 
the 
tenor. 
The 
Clarity 
Enhancer 
module 
requests 
verbs 
and 
adjectives 
associated 
with 
the 
vehicle 
from 
mined 
literary 
corpora. 
Finally, 
the 
sentence 
is 
completed 
by 
performing 
imaginative 
recall 
with 
known 
valid 
sentence 
constructions 
for 
metaphor 
identified 
from 
literary 
corpora. 



Table 
1: 
Comparison 
1 
of 
FIGURE8 
and 
human 
rankings 
for 
clarity 
and 
overall 
quality. 
In 
this 
set, 
FIGURE8 
was 
asked 
to 
generate 
and 
rank 
figurative 
descriptions 
given 
“pearl” 
as 
the 
tenor. 
Human 
clarity 
and 
likability 
rankings 
were 
found 
to 
be 
highly 
correlated 
(. 
= 
0.684). 
Spearman 
analyses 
also 
indicated 
positive 
correlations 
between 
human 
and 
FIGURE8 
rankings 
(clarity: 
. 
= 
0.872; 
quality: 
. 
= 
0.821). 


man 
ratings. 
This 
can 
be 
assessed 
by 
requesting 
humans 
to 
rank 
descriptions 
generated 
by 
the 
FIGURE8 
algorithm, 
and 
determining 
if 
the 
majority 
are 
in 
agreement 
with 
the 
computer’s 
(FIGURE8’s) 
ranking. 
A 
pilot 
study 
indicated 
that 
providing 
each 
description 
with 
additional 
context 
would 
make 
the 
ranking 
process 
too 
time-consuming 
for 
participants. 
Thus, 
functions 
to 
enhance 
aptness 
were 
not 
included 
when 
generating 
outputs 
to 
be 
evaluated 
in 
the 
full-scale 
study. 


Method 


One 
hundred 
participants 
(73 
female, 
27 
male) 
were 
recruited 
via 
Amazon’s 
Mechanical 
Turk. 
Each 
participant 
viewed 
a 
series 
of 
five 
sentences 
at 
a 
time, 
and 
were 
asked 
to 
rank 
the 
similes 
by 
how 
understandable 
they 
were 
(clarity), 
and 
by 
how 
much 
they, 
as 
individuals, 
enjoyed 
the 
comparison 
(likability). 
Each 
set 
of 
five 
sentences 
contained 
the 
same 
tenor, 
and 
were 
originally 
generated 
and 
ranked 
by 
FIGURE8. 
The 
sets 
were 
not 
hand-selected 
by 
the 
author. 
That 
is, 
the 
first 
eleven 
sets 
FIGURE8 
generated 
and 
ranked 
were 
used 
in 
the 
study. 


Results 


Human 
preferences 
were 
determined 
by 
following 
the 
majority 
criterion. 
As 
seen 
in 
Figures 
4 
and 
5, 
human 
clarity 
ratings 
were 
often 
positively 
correlated 
with 
overall 
quality 
ratings, 
and 
this 
correlation 
was 
confirmed 
with 
Spear-
man 
analyses. 
Overall, 
FIGURE8’s 
top 
result 
for 
clarity 
and 
overall 
quality 
generally 
agreed 
with 
the 
human 
rankings 
for 
each 
of 
the 
eleven 
sets. 
FIGURE8 
exactly 
matched 
the 
first 
ranking 
46% 
of 
the 
time 
for 
clarity 
and 
likability. 
Further, 
it 
matched 
either 
the 
first 
or 
second 
ranking 
82% 
and 
100% 
of 
the 
time 
for 
the 
clarity 
and 
likability 
categories, 
respectively. 
Examples 
of 
how 
FIGURE8 
matched 
human 
ratings 
are 
shown 
in 
Tables 
1, 
2, 
and 
3. 


Discussion 
and 
Future 
Work 


In 
this 
paper, 
the 
author 
has 
introduced 
the 
FIGURE8 
system 
as 
a 
novel 
tool 
for 
generating 
and 
evaluating 
creative 
figurative 
descriptions. 
FIGURE8’s 
assessments 
are 
grounded 
in 
psychological 
models 
of 
metaphor 
comprehension, 
and 
have 
thus 
far 
been 
found 
to 
adequately 
match 
human 
rankings 
when 
agreed 
upon. 


Participants 
in 
the 
evaluation 
portion 
were 
not 
told 
that 
the 
descriptions 
were 
generated 
by 
a 
computer. 
Only 
two 


Proceedings of the Sixth International Conference on Computational Creativity June 2015 


Table 
2: 
Comparison 
2 
of 
FIGURE8 
and 
human 
rankings 
for 
sentences 
of 
tenor 
“snow”. 
Human 
clarity 
and 
likability 
rankings 
were 
found 
to 
be 
positively 
correlated 
(. 
= 
0.763). 
Spearman 
correlation 
analysis 
suggested 
that 
FIGURE8 
clarity 
rankings 
were 
positively 
associated 
with 
human 
clarity 
rankings 
(. 
= 
0.872), 
but 
no 
significant 
association 
was 
found 
between 
likability 
rankings 
in 
this 
case 
(. 
= 
-0.359). 



Table 
3: 
A 
third 
comparison 
of 
FIGURE8 
and 
human 
rankings 
for 
sentences 
of 
tenor 
“queen”. 
In 
addition 
to 
showing 
first 
choice 
rankings, 
this 
table 
displays 
human 
rankings 
when 
considering 
first 
and 
second 
choices. 
That 
is, 
“the 
queen 
stands 
like 
a 
strong 
castle” 
was 
ranked 
as 
either 
first 
or 
second 
for 
the 
majority 
of 
respondents. 
In 
both 
cases, 
human 
clarity 
and 
likability 
rankings 
were 
found 
to 
be 
positively 
correlated 
(. 
> 
0.9). 
Spearman 
analyses 
also 
suggested 
for 
both 
cases 
that 
FIGURE8 
and 
human 
rankings 
for 
clarity 
and 
likability 
were 
positively 
correlated 
with 
high 
significance 
(. 
> 
0.7). 



Figure 
4: 
First 
choice 
rankings 
for 
the 
generated 
set 
of 
sentences 
using 
pearl 
as 
the 
tenor. 
Although 
some 
disparities 
existed, 
the 
majority 
of 
respondents 
generally 
agreed 
upon 
which 
sentence 
was 
the 
most 
understandable. 


comments 
were 
made 
about 
checking 
sentences 
for 
validity 
prior 
to 
including 
them 
in 
the 
study, 
and 
one 
regarding 
how 
painful 
it 
was 
to 
rank 
“bad 
poetry”. 
Most 
participants, 
however, 
enjoyed 
the 
task 
and 
provided 
positive 
feedback 
about 
their 
experience 
(“cool 
hit”,“super 
fun”,“I 
love 
this”). 
It 
is 
conceivable 
that 
task 
enjoyment 
affected 
user 
responses, 
but 


controlling 
for 
explicit 
indication 
of 
task 
enjoyment 
yielded 
no 
significant 
difference 
in 
the 
results. 
Controlling 
for 
gender 
also 
did 
not 
reveal 
significantly 
different 
outcomes. 


Interestingly, 
for 
roughly 
half 
(50-60% 
per 
set) 
of 
the 
participants, 
how 
much 
they 
liked 
the 
figurative 
description 
was 
directly 
correlated 
with 
how 
well 
they 
understood 
it. 
The 
most 
highly 
ranked 
phrases 
for 
clarity 
were 
also 
often 
ranked 
first 
for 
likability, 
and 
the 
Spearman 
coefficient 
was 
used 
to 
confirm 
these 
positive 
associations. 
This 
was 
a 
surprising 
finding, 
because 
more 
variation 
and 
subjectivity 
was 
expected 
for 
these 
ratings. 
Discrepancies 
between 
human 
and 
FIGURE8 
likability 
rankings, 
such 
as 
in 
Table 
2, 
could 
potentially 
be 
explained 
by 
a 
human 
tendency 
to 
prefer 
metaphors 
containing 
words 
of 
positive 
sentiment 
value. 
However, 
more 
analysis 
is 
required 
to 
confirm 
this 
idea, 
and 
further 
study 
is 
needed 
to 
evaluate 
how 
qualities 
of 
language 
are 
weighted 
across 
general 
and 
expert 
populations. 
Judging 
from 
participant 
comments, 
it 
is 
also 
possible 
that 
some 
people 
may 
like 
metaphors 
primarily 
based 
on 
qualities 
other 
than 
clarity 
(such 
as 
prosody, 
sentiment, 
or 
whimsy). 
If 
these 
groups 
could 
be 
automatically 
identified, 
perhaps 
future 
computer-produced 
descriptions 
could 
adapt 
to 
generate 
more 
personalized 
descriptions 
for 
the 
optimum 
enjoyment 
of 
the 
reader. 


While 
FIGURE8 
is 
able 
to 
rank 
its 
figurative 
descriptions 
over 
various 
measures 
of 
quality, 
how 
well 
its 
output 
com-

Proceedings of the Sixth International Conference on Computational Creativity June 2015 


Figure 
5: 
Clarity 
rankings 
for 
the 
generated 
set 
of 
sentences 
using 
snow 
as 
the 
tenor. 
Participants 
rated 
what 
FIGURE8 
considered 
the 
most 
unsurprising 
metaphor 
as 
the 
most 
clear, 
but 
there 
was 
no 
highly 
significant 
consensus 
regarding 
the 
most 
likable 
description. 


pares 
with 
human-authored 
descriptions 
was 
not 
assessed. 
The 
fact 
that 
most 
participants 
in 
the 
evaluation 
did 
not 
question 
the 
source 
of 
the 
texts 
is 
a 
promising 
sign 
that 
the 
system 
presented 
here 
generates 
human-like 
output. 
Regardless, 
its 
present 
constructions 
can 
be 
automatically 
assigned 
rankings 
on 
par 
with 
human 
evaluations. 
It 
is 
assumed 
that 
as 
the 
quality 
of 
FIGURE8’s 
generations 
increases, 
it 
will 
be 
able 
to 
extract 
the 
best 
output 
from 
the 
results 
of 
its 
“brainstorming”. 
Future 
research 
should 
build 
upon 
this 
foundation 
and 
work 
towards 
evaluating 
computer-generated 
descriptions 
in 
terms 
of 
aptness, 
prosody, 
and 
unpredictability. 
When 
machines 
are 
fully 
able 
to 
grasp 
the 
subtleties 
and 
aesthetics 
of 
figurative 
language, 
we 
as 
humans 
will 
be 
able 
to 
relate 
to 
them 
as 
never 
before. 


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