#  PINEAPPLE: Personifying INanimate Entities by Acquiring Parallel
Personification Data for Learning Enhanced Generation

Sedrick Scott Keh 1, Kevin Lu 2, Varun Gangal 1, Steven Y. Feng11footnotemark:
1 3,
Harsh Jhamtani 1, Malihe Alikhani 4, Eduard Hovy 1
1Carnegie Mellon University, 2University of Waterloo,
3Stanford University, 4University of Pittsburgh
<EMAIL_ADDRESS><EMAIL_ADDRESS>
<EMAIL_ADDRESS><EMAIL_ADDRESS>Equal contribution by Varun and Steven

###### Abstract

A personification is a figure of speech that endows inanimate entities with
properties and actions typically seen as requiring animacy. In this paper, we
explore the task of personification generation. To this end, we propose
PINEAPPLE: Personifying INanimate Entities by Acquiring Parallel
Personification Data for Learning Enhanced Generation. We curate a corpus of
personifications called PersonifCorp, together with automatically generated
de-personified literalizations of these personifications. We demonstrate the
usefulness of this parallel corpus by training a seq2seq model to personify a
given literal input. Both automatic and human evaluations show that fine-
tuning with PersonifCorp leads to significant gains in personification-related
qualities such as animacy and interestingness. A detailed qualitative analysis
also highlights key strengths and imperfections of PINEAPPLE over baselines,
demonstrating a strong ability to generate diverse and creative
personifications that enhance the overall appeal of a sentence. 111Data and
code can be found at https://github.com/sedrickkeh/PINEAPPLE

##  1 Introduction

Personification is the attribution of animate actions or characteristics to an
entity that is inherently inanimate. Consider, for example, the sentence “The
stars danced playfully in the moonlit sky.” Here, the vibrance of the stars
(something inanimate) is being likened to dancing playfully, which is a
distinctly animate action. By allowing readers to construct clearer mental
images, personifications enhance the creativity of a piece of text Bloomfield
(1980); Dorst (2011); Flannery (2016).

Being able to automatically identify and generate personifications is
important for multiple reasons. First, humans naturally use personifications
when communicating. When we say something like “My phone has died,” or “My car
is not cooperating,” to a dialogue system, it is important that the dialogue
system understands the intended meaning behind these personifications. If
these systems interpret personifications literally, they may fail in several
downstream tasks (e.g. classification) since their understanding is incorrect.
Being able to generate personifications also allows dialogue agents and
language models to be more creative and generate more figurative sentences.
Personification generation has additional applications such as AI-assisted
creative writing, since machine-generated figures of speech have been shown to
enhance the interestingness of written text Chakrabarty et al. (2021).

Figure 1: Overall PINEAPPLE model pipeline. The left part of the diagram shows
the corpus creation process, while the right part of the diagram shows the
training and generation process.

Despite previous success in generating other figures of speech such as similes
Chakrabarty et al. (2020), metaphors Stowe et al. (2021), hyperboles Troiano
et al. (2018), irony Van Hee et al. (2018), and sarcasm Hazarika et al.
(2018); Jaiswal (2020), personification generation is relatively
underexplored. One key challenge is that personifications do not have an
explicit syntactic structure unlike similes which use ‘like’ or ‘as’. They are
also not as loosely-defined as metaphors. Rather, a personification requires
identifying an inanimate subject together with actions or descriptions which
are commonly used on animate subjects. These steps are challenging and require
our models to understand commonsense concepts including animacy.

In line with exploring the task of personification generation, we present
three main contributions: (1) We curate a dataset, PersonifCorp, of diverse
personification examples from various sources. (2) We propose a method called
PINEAPPLE to automatically de-personify personifications and create a parallel
corpus of personification data along with their literalizations. (3) Given our
parallel corpus, we train a seq2seq model to personify given text. We conduct
automatic and human evaluation and qualitative analysis of the generated
outputs.

##  2 Datasets

We curate a dataset called PersonifCorp of 511 personifications, with 236
coming from a publicly available open-sourced
list222https://www.kaggle.com/datasets/varchitalalwani/figure-of-speech and
275 manually-filtered personifications extracted from the Deja dataset Chen et
al. (2015). The Deja dataset is an image-captioning dataset containing a
“figurative” subset of size 6000, of which $4.1\%$ of the captions are
labelled as personifications. We extract these personifications and combine
them with our existing list to form the final PersonifCorp dataset.

We also note that although it is possible to further expand this dataset (e.g.
by ad hoc searching for miscellaneous sites and examples online), we
ultimately decide against this after performing an initial investigation. When
we attempted to look for additional examples, we found that many of the new
examples we found were near-duplicates of existing personifications already in
our list. In addition, ad hoc searching can give at most a few hundred
examples, which will lead to very incremental gains in performance. This is
impractical if we want to collect a large-scale dataset. We hence decided to
restrict ourselves to sentences from reasonably well-vetted, already existing
corpora from *CL prior art or officially released data from sources like
Kaggle/SemEval shared tasks.

###  2.1 Characterizing Personifications

We define the elements of personification, an analogue to what was previously
done for similes Niculae and Danescu-Niculescu-Mizil (2014); Chakrabarty et
al. (2020). While similes could be decomposed into very granular structures
and well-defined elements, the unstructured nature of personifications
prevents us from directly defining such fine-grained elements for
personifications. Rather, we define two main high-level elements, the topic (a
noun phrase that acts as logical subject) and the attribute (the distinctly
animate action or characteristic that is being ascribed to the topic). Figure
2 shows examples of how these topics and attributes can relate to each other.

###  2.2 Automatic Parallel Corpus Construction

In order to train a seq2seq model to generate high-quality personifications,
we need pairs of personifications along with their corresponding
literalizations. However, the literalization process may take several human-
hours, which is impractical for large datasets. We therefore propose
PINEAPPLE, a three-stage automatic de-personification process, where we first
identify all valid topic-attribute pairs, then generate multiple candidates to
replace the attribute of each topic. Lastly, we select the most appropriate
candidate in terms of animacy, fluency, and meaning preservation. These steps
are further detailed individually below:

Topic-Attribute Extraction. To identify the topics and attributes, we consider
the dependency parse tree of a sentence and the part-of-speech (POS) tags of
each of its words. Given the tree, we extract all the nouns/pronouns which
have edges pointing into it with the nominal subject label, together with the
corresponding parent nodes. For instance, in the sentence “The stars danced in
the night sky”, the word ‘danced’ is a parent of the word ‘stars’, with the
nominal subject edge relationship. We can thus identify ‘stars’ as the topic
and ‘danced’ as the attribute. In more complex scenarios, we may need to
perform some additional merging to deal with compound multi-word topics and
attributes, as well as any additional modifiers. More specifically, using the
POS tags, we identify all words tagged as negation modifiers, possession
modifiers, nominal modifiers, adjectival complements, and objects of
prepositions, and words tagged as determiners and parts of compound
phrases.333The spaCy library was used to extract the dependency tree and POS
tags. After extracting these nodes, they are iteratively merged with their
parents in the dependency parse tree, and the merging process is performed
repeatedly until no more merges are possible. The final topic-attribute pairs
are then identified using the nominal subject edge relationship as previously
described. Examples of the merging process can be found in Appendix A.1.

|  ATTRIBUTE Type
---
Example
Noun |  | The planet earth is our mother.
---
Verb |  | My alarm clock yells at me to
---
get out of bed every morning.
Adjective |  |  Justice is blind and, at times, deaf.
---

Figure 2: Examples of different types of personification attributes (topics in red and attributes in blue). Figure 3: Overview of the PINEAPPLE de-personification pipeline. Original Personification |  Result After De-Personifying
---|---
How far that little candle throws its beams! |  How far that little candle can spread its beams!
A book is a fragile creature, it suffers the wear of time, it fears rodents, the elements and clumsy hands. |  A book is fragile, it can break from the wear of time, it can be eaten by rodents, the elements and clumsy hands.
The camera loves her since she is so pretty. |  The camera is always on her since she is so pretty.
Any trust I had for him walked right out the door. |  Any trust I had for him had gone right out the door.
The full moon peeped through partial clouds. |  The full moon was visible through partial clouds.
Opportunity was knocking at her door. |  Opportunity was knocking at her door.
The killing moon will come too soon. |  The killing moon will be here too soon.
Table 1: Example outputs of the PINEAPPLE de-personification pipeline. The
attributes are highlighted in blue for both the original personifications, as
well as the de-personified output sentences. The last two rows contain
negative examples where the process does not successfully de-personify the
input.

Candidate Generation. Once the topic-attribute pairs have been identified, we
then determine which topics are inanimate. To achieve this, we need some type
of commonsense notion of what constitues animacy. We use COMET Bosselut et al.
(2019) to tap into the commonsense knowledge present in large-scale knowledge
graphs such as ConceptNet Speer et al. (2017). Although ConceptNet has no
explicit notion of animacy, it has certain edge relations that we can leverage
to design a proxy metric. More specifically, we use the IsA relation to design
a custom IsAPerson animacy metric. If the topic of our sentence refers to an
animate entity, then we expect its IsA relation score with the word ‘human’ to
be relatively low.444For the COMET ConceptNet graph, lower scores correspond
to better matches. The IsAPerson metric is hence defined as follows: given a
topic, we compute and average its IsA scores to various words that are
synonymous or very closely related to ‘human’, such as ‘person’, ‘man’, and
‘woman’. We call this set of ‘human’-related words the humanset. The
construction and full list of words in the humanset can be found in Appendix
A.2. The average of these ConceptNet scores is then our final IsAPerson
animacy score.

Phrases whose IsAPerson animacy score exceeds a certain threshold 555We use a
threshold of 7.0 for the IsAPerson animacy metric. IsAPerson scores $<$ 7.0
are considered animate, while scores $\geq$ 7.0 are considered inanimate. More
details regarding the selection of this threshold can be found in Appendix
A.3. are considered animate; otherwise, they are considered inanimate. Since
our goal is to de-personify a sentence, we can safely discard all the animate
topics, as these need no further de-personification. Rather, we focus on the
inanimate topics because the segment we want to de-personify most likely
occurs in the topic-attribute pairs whose topic is inanimate. Once we identify
all such inanimate topic-attribute pairs, we mask out the attribute of each of
them with <mask>, then use a pre-trained BART model Lewis et al. (2020) to
generate the top $k=10$ candidates for each mask using beam search with a beam
size of 10. The goal of this process is to replace a possibly animate
action/characteristic with candidates that are inanimate.

Candidate Selection. Given $k=10$ candidate replacement attributes, we now
select the most ideal replacement based on three metrics: animacy, fluency,
and meaning preservation.

  1. 1.

Animacy – We want the replacement attribute to be inanimate; otherwise we
would just be replacing an animate attribute with another animate attribute.
We define the animacy of a topic-attribute pair as difference between the
affinity for a human ($\mathcal{A}_{human,\textsc{att}}$) to do/possess the
attribute, and the affinity for the given topic
($\mathcal{A}_{\textsc{topic},\textsc{att}}$) to do/possess the attribute. We
use COMET’s ConceptNet relations to compute these affinities; specifically, we
use the CapableOf relation. To approximate $\mathcal{A}_{human,\textsc{att}}$,
we compute the average CapableOf score between the given attribute and all
words in our previously defined humanset. To compute
$\mathcal{A}_{\textsc{topic},\textsc{att}}$, we compute the CapableOf score
between the topic and its attribute. The final animacy score of a topic-
attribute pair is defined as the difference
$\mathcal{A}_{human,\textsc{att}}-\mathcal{A}_{\textsc{topic},\textsc{att}}$.
If there are multiple topic-attribute pairs, we consider the average animacy
of all pairs.

  2. 2.

Fluency – The de-personified sentences should be grammatically correct and
sound natural. To measure for fluency, we use BART’s generation scores (i.e.
sum of individual token logits in the generated output).

  3. 3.

Meaning Preservation – It is important that the de-personified sentence does
not stray too far from the meaning of the original personification. We use
BERTScore Zhang* et al. (2020) between the de-personified and original
sentences to measure meaning preservation.

We design a composite scoring metric comprised of the aggregate scores from
these 3 metrics. Due to scaling differences, we consider the $\log$ of the
animacy score. To account for the fact that lower animacy scores imply less
animate topic-attribute pairs (which is desirable in de-personification), we
take the negative of the animacy. More precisely, we define our candidate
score $S_{i}$ for candidate $i$ as

$S_{i}=\alpha\cdot(-\log(S_{anim.}))+\beta\cdot S_{flue.}+\gamma\cdot
S_{mean.}$

where $\alpha,\beta,\gamma$ are parameters. 666We use $\alpha=1$, $\beta=1$,
$\gamma=1$. Details about the tuning and selection of $\alpha,\beta,\gamma$
can be found in Appendix A.3.

Once $S_{i}$ is computed for all candidates, we select the candidate with the
highest composite score as our final de-personified sentence. A diagram of the
entire PINEAPPLE pipeline is shown in Figure 3, and example outputs can be
found in Table 1.

###  2.3 Test Data Construction

While automatically generated pairs of personifications and literal de-
personifications may greatly assist with training, these may not necessarily
be accurate for testing. Rather, it would be more ideal during testing if we
have ground-truth human-annotated data. To mimic our task at hand, we gather a
list of non-personified English
sentences.777https://github.com/tuhinjubcse/SimileGeneration-EMNLP2020#set-up-
data-processing-for-simile We then select two annotators who are native
English speakers currently enrolled in a university with English as a medium
of instruction. These annotators were instructed to manually personify these
sentences to create ground-truth reference personifications. The final
PersonifCorp test split has 72 literal + personified sentence pairs.

##  3 Experimental Setup

###  3.1 Methods

Below we outline the three models we consider, with two of them being naive
baselines (COMET and Baseline-BART) that we simply use on PersonifCorp’s test
set, and the third (Finetuned-BART) being our proposed model trained on
PersonifCorp.

  1. 1.

COMET: We extract the topic-attribute pairs and identify the inanimate topics
using the methods detailed in §2.2. Instead of generating candidate
replacements using BART like in §2.2, we generate candidates by considering
the top $k=10$ results for a given topic using COMET’s ConceptNet IsCapable
relation (if the original attribute is a verb) or HasProperty relation (if
adjective or adverb). To incorporate a notion of animacy, we use the
previously defined attribute animacy $\mathcal{A}_{human,\textsc{att}}$ and
select the candidate with highest animacy as our final replacement.

  2. 2.

Baseline-BART (BL-BART): We imitate the process outlined for the COMET
baseline, except we use a pretrained BART model to generate the candidates
instead of using COMET. All other steps (topic-attribute extraction and
candidate selection) remain the same.

  3. 3.

PINEAPPLE-BART (PA-BART): We fine-tune a BART model by supplying the
PersonifCorp train split literal de-personified sentences (from the PINEAPPLE
pipeline) as inputs, and the original ground-truth personifications as target
outputs. This is trained as a seq2seq task. During generation, we use beam
search. Further details are outlined in §3.3.

###  3.2 Evaluation

We consider both automatic evaluation metrics (§3.2.1) and human evaluation
(§3.2.2).

####  3.2.1 Automatic Evaluation

For each model in §3.1, we evaluate its generated outputs on PersonifCorp’s
test split using each of the following automatic evaluation metrics:

  1. 1.

BLEU Papineni et al. (2002): We use BLEU to ensure that the generations do not
greatly differ from the inputs. We compute the BLEU score of each generated
output with the literal inputs (for meaning preservation), as well as the
ground-truth reference personifications.

  2. 2.

BERTScore Zhang et al. (2019): BERTScore measures how semantically related two
sentences are, and is generally more robust than BLEU. We compute the
BERTScore of each generated output with the inputs, as well as the ground-
truth reference personifications.

  3. 3.

Fluency: To approximately measure the fluency of a sentence, we use generation
(log-perplexity) losses of each output using the GPT-2 language model Radford
et al. (2019).

  4. 4.

Animacy: We are interested in how personified the generated output is. We use
the same animacy metric used for candidate selection in §2, which is a
combination of how animate the attribute is, as well as how inanimate the
topic is. More precisely, this is defined as
$\mathcal{A}_{human,\textsc{att}}-\mathcal{A}_{\textsc{topic},\textsc{att}}$,
where the $\mathcal{A}$ animacy scores are previously defined in §2.

####  3.2.2 Human Evaluation

The human evaluation was conducted using paid annotators on Amazon Mechanical
Turk (AMT). Annotators were from Anglophone countries with $>97$% approval
rate.888More details about the human eval are in Appendix B.1. Each test
example was evaluated by exactly 2 annotators. For each test example, we first
generate outputs using each of the methods outlined in §3.1. Corresponding to
this test instance, we then create an AMT task page (a HIT), presenting the
input literal sentence and each of the method outputs (in randomized order)
for annotation along five aspects of text quality.

Specifically, annotation was elicited for the following metrics: (1)
Personificationhood (“To what extent does the new sentence contain a
personification?”), (2) Appropriateness (“Do the personified nouns, verbs,
adjectives, adverbs sound mutually coherent and natural?”), (3) Fluency (“Does
it sound like good English with good grammar?”), (4) Interestingness (“How
interesting and creative a rephrasing of the original sentence is the
personified sentence?”), and (5) Meaning Preservation (“Do the entities, their
actions, interactions, and the events appear and relate to each other in the
same way as in the original sentence?”). Each metric was scored on a Likert
scale, with 1 being the lowest and 5 being the highest.

For Interestingness, we observed poor agreement scores amongst the AMT
annotators.999Further details on inter-annotator agreement scores can be found
in Appendix B.2. Hence, for this aspect, we instead used a curated group of
known, in-person annotators: a cohort of three native English-speaking
students from an American university. Amongst these annotators, we observe a
considerably higher agreement, with a Krippendorff $\alpha$ value of $0.5897$.
For selecting this cohort from a slightly larger pool of candidates, we
assessed their performance on a short qualification test of basic English
literary skills and knowhow. The final cohort chosen each scored 85% or higher
on this test. Further details are in Appendix B.3.

###  3.3 Implementation Details

The PersonifCorp training corpus was randomly split into a training and
validation split with an 80-20 ratio. We fine-tune a BART-base model with 139M
parameters using a learning rate of 2e-5 and a batch size of 4. Training was
done for 20 epochs and 400 warmup steps, and model/epoch selection was
performed based on the lowest validation loss. For generating the outputs,
decoding was done using beam search with a beam size of 10. Additional details
can be found in Appendix C.

##  4 Results and Analysis

###  4.1 Automatic Evaluation Results

| BLEU | BERTScore |  |
---|---|---|---|---
| Input | Gold | Input | Gold |  Fluency $\downarrow$ | Animacy
Human Annotation | 0.172 | 1.000 | 0.749 | 1.000 | 5.264 | 0.332
COMET | 0.127 | 0.128 | 0.655 | 0.569 | 6.366 | -2.028
BL-BART | 0.132 | 0.133 | 0.728 | 0.617 | 4.573 | 0.106
PA-BART | 0.153 | 0.160 | 0.748 | 0.636 | 5.460 | 0.679
Table 2: Average automatic evaluation results. The best-scoring method for each metric is highlighted in bold. Higher scores are better for all metrics except for fluency. | Personificationhood | Appropriateness | Fluency | Interestingness | Meaning Preservation
---|---|---|---|---|---
Human Annotation | 3.763 | 4.175 | 4.138 | 3.667 | 3.913
COMET | 3.525 | 3.563 | 3.738 | 1.801 | 3.550
BL-BART | 3.500 | 3.938 | 4.188 | 2.006 | 3.750
PA-BART | 3.738 | 4.000 | 4.138 | 2.782 | 3.875
Table 3: Average human evaluation results. The best-scoring method for each
metric is highlighted in bold.

Table 2 reports the automatic evaluation results for each of the metrics
detailed in §3.2.1. We observe that our PA-BART model performs best across all
automatic metrics except for fluency, where BL-BART performs best. The
difference in performance is most significant in the Animacy metric, which is
the key metric that quantifies the degree to which a sentence is personified.
This confirms that indeed, our proposed PINEAPPLE method is successful in
training a model to personify text.

Our PA-BART model also performs well for both BLEU and BERTScore, scoring
better than the COMET and BART baselines, and coming second only to the human-
written personifications.

Lastly, with regards to fluency, the BL-BART model outperforms the PA-BART
model. This is likely because when considering GPT-2 likelihood, it may
unfavorably penalize creative sentences with personifications since these are
naturally less common in regular text. As an example, the sentence “The stars
danced playfully” (GPT-2 loss = 7.02) would be deemed significantly less
fluent than the sentence “The stars twinkled brightly” (GPT-2 loss = 5.24),
even though they are both valid sentences with similar meanings. This argument
is further supported by the fact that even the reference human-generated
personifications received a lower fluency score than the BL-BART outputs.
Further, literal sentences are indeed typically more fluent overall than
personifications since they express the meaning literally. Nevertheless, we
are still interested in the other qualities being measured by fluency: Is the
sentence coherent? Does it make unnecessary grammatical errors? In this
regard, the fluency of PA-BART remains quite good. It is significantly better
than the fluency of the COMET personifications and only slightly worse than
the fluency of the human-written personifications.

###  4.2 Human Evaluation Results

Human evaluation results are reported in Table 3. Out of the five human
evaluation metrics, the most pertinent metric to the personification
generation task is Personificationhood, as this metric explicitly tries to
quantify the presence and overall quality of personifications. In this metric,
our PA-BART model performs significantly better than both baselines and is
only slightly worse than the human reference personifications. This indicates
that PA-BART is very successful in generating personifications that humans are
able to detect and understand.

Aside from measuring the presence of personifications, we also want to measure
more fine-grained qualities of these personifications. This is done by
considering the Appropriateness and Interestingness scores. In
Interestingness, PA-BART significantly outperforms both baselines but is worse
than human annotations, while in Appropriateness, PA-BART slightly outperforms
BL-BART and is slightly worse than human annotations. Overall, we can conclude
that the personifications generated by PA-BART are of good quallity: the
attributes match up well with the topics, and they are overall very creative.
This is further exemplified through the qualitative examples explored in §4.3.

Observations from Meaning Preservation and Fluency are very similar to those
from the BLEU/BERTScore/Fluency metrics in the automatic evaluations. For
Meaning Preservation, PA-BART performs best among all models, and only
slightly trails human references. Meanwhile, for fluency, BL-BART was ranked
the most fluent, outperforming both PA-BART and the human references. As
discussed previously, this is likely due to the fact that literal sentences
are generally perceived to be more fluent than personifications.

###  4.3 Qualitative Analysis

Method |  Text
---|---
Literal Input |  You are at a business dinner with your boss when your phone rings out loud (ex.1)
Human Ref |  You are at a business dinner with your boss when your phone starts singing out loud
COMET |  You are at a business dinner with your boss when your phone beep out loud
BL-BART |  You are at a business dinner with your boss when your phone rings and you answer out loud
PA-BART |  You are at a business dinner with your boss when your phone yells out loud
Literal Input |  In most horror settings, silence is key. (ex.2)
Human Ref |  In most horror settings, silence is the protagonist.
COMET |  In most horror settings, silence scary.
BL-BART |  In most horror settings, silence is preferred.
PA-BART |  In most horror settings, silence is a ghost.
Literal Input |  Her relationships with family and friends are very difficult (ex.3)
Human Ref |  Her relationships with family and friends behave very unusually
COMET |  Her relationships with family and friends serious
BL-BART |  Her relationships with family and friends have always been strong.
PA-BART |  Her relationships with family and friends are very lonely
Literal Input |  Then there weren’t any more parties as the house became silent (ex.4)
Human Ref |  Then there weren’t any more parties as the house kept mum.
COMET |  Then there weren’t any more parties as the house build.
BL-BART |  Then there weren’t any more parties as the house fell into disrepair.
PA-BART |  Then there were no more parties as the house lamented.
Literal Input |  It was a moonless nights, the air was still and the crickets were silent (ex.5)
Human Ref |  It was a moonless nights, the air was asleep and the crickets were silent
COMET |  It cold outside a moonless nights, the air cold outside still and the crickets noisy
BL-BART |  It was a moonless nights, the air was still and the crickets were calling.
PA-BART |  It was one of those moonless nights, the air was tired and the crickets were silent
Literal Input |  The sound hit Frank loud enough to make your ear hurt (ex.6)
Human Ref |  The sound slapped Frank loud enough to make your ear hurt
COMET |  The sound echo Frank loud enough to make your ear sense sound
BL-BART |  The sound of Frank Sinatra is loud enough to make your ear ring.
PA-BART |  The sound clapped loud enough to make your ear cry

Table 4: Qualitative examples for personification: literal input, human
writing, COMET, BL-BART, and PA-BART. More can be found in Appendix D.

Table 4 contains a list of color-coded qualitative examples for each method.
In Figure 2, we previously outlined three main types of personification topic-
attribute pairs, namely the cases where attribute is a noun, a verb, and an
adjective. The first three examples in Table 4 demonstrate the capacity of our
PA-BART model to capture all three cases. In the first example, the literal
verb in “your phone rings out loud” is replaced with the more appropriate
animate verb in “your phone yells out loud.” In the second, “silence is key”
is replaced with a noun in “silence is a ghost”, while in the third example,
the literal adjective “very difficult” is replaced with the animate adjective
“very lonely”. These examples illustrate the generative flexibility of our
model and its capacity to generate diverse outputs with different parts-of-
speech.

We also observe that the outputs for PA-BART generally capture the meaning of
the original text (and surrounding context) more accurately than the other
baselines. In fact, the personifications greatly enhance the expressiveness of
some of these sentences. In the first example, PA-BART replaces ‘rings’ with
‘yells’, while COMET replaces it with ‘beeps’, and BL-BART leaves ‘rings’
unchanged and just adds more details. Given the context of the sentence, we
see that ‘yells’ is more appropriate, expressive, and consistent with the
context. A similar argument can be made for most of the other examples in the
table: for the third example, PA-BART replaces the literal “very difficult”
with the much more animate and expressive “very lonely”, which is a suitable
word to describe a relationship. In the fourth example, the BL-BART model is
able to successfully capture the meaning of “the house became silent” with
“the house fell into disrepair”. Although the meaning is correct, “fell into
disrepair” is more literal and does not contain a personification. Compare
this with the PA-BART’s choice to replace “the house became silent” with “the
house lamented”, which fits with the overall context (“Then there were no more
parties…”), and also greatly enhances creativity by invoking the vivid image
of lamentation. Meanwhile, in the fifth example, BL-BART personifies “the
crickets were silent” with “the crickets were calling”. However, this shift
completely changes the meaning, so it is a rather poor choice of
personification. In contrast, PA-BART rewrites “the air was still” as “the air
was tired”, which is a reasonable personification that is consistent with the
imagery in the sentence (“moonless nights”, “crickets were silent”). Hence, we
see that PA-BART can generate creative and meaningful personifications, while
simultaneously staying true to the spirit of the sentence.

We also point out that our model is not limited to single-word substitutions.
Rather, it considers a holistic view of the entire sentence and modifies key
segments as necessary. This allows PA-BART to handle compound phrases well:
consider, for instance, the one-to-many-word substitution of ‘key’
$\longrightarrow$ ‘a ghost’ (example 2), and the many-to-one-word substitution
of “became silent” $\longrightarrow$ “lamented” (example 4). More importantly,
PA-BART is also able to simultaneously generate personifications in two
disjoint parts of the sentence, as seen in the last example: “The sound
clapped loud enough to make your ear cry.” Here, there are two
personifications in “sound hit” $\longrightarrow$ “sound clapped”, and “ear
hurt” $\longrightarrow$ “ear cry”.

This last example also demonstrates the imperfection of our method. Although
the model is able to generate two personifications, it loses a component of
the original sentence because the recipient of the action (‘Frank’) has
disappeared. This same issue of meaning or information loss is present in
example 2, where our model’s output of “silence is a ghost”, while a
personification, actually contradicts the original text “silence is key”. BL-
BART’s output of “silence is preferred”, while not a personification,
correctly preserves the original meaning, as does the human reference of
“silence is the protagonist”. This suggests that the model may still need some
improvements with balancing creativity and semantic preservation. Other
possible weaknesses are outlined in §6.

####  4.3.1 Novelty and Diversity Analysis

We randomly sample 30 examples from the PA-BART generations- and manually
identify the parts of the sentences that were personified, as well as the
animate attributes used to personify the topics. Among the 30 examples, there
were 27 unique attributes, and only 3 repeats. Additionally, there were 9
examples which generated completely new attributes that were never before seen
in the training set, which demonstrates that the model is able to sufficiently
capture the essence of a personification, rather than just blindly memorizing
attributes from the training data.

##  5 Related Work

We present the linguistic underpinnings behind the topic-attribute framework
used in this paper and explore how other types of figures of speech are
generated. We also explore what makes personification generation so
challenging.

Linguistic Motivations. Personifications traditionally do not have clearly
defined classifications. In fact, even within the linguistic community, the
definition of a personification is not always very clear-cut Edgecombe (1997);
Hamilton (2002). A study by Long (2018) examines the personification structure
“nonhuman subject + predicate verb (used for humans only) + others,” as well
as the structure “others + predicate verb (used for humans only) + nonhuman
object + others.” We generalize and repackage these concepts, renaming the
subject as the topic and the predicate verb as the attribute. In doing so, we
are able to capture more general notions of animacy beyond just verbs.

Generation of Metaphors, Similes, etc. A lot of studies on metaphors have
focused on identification using techniques like word sense disambiguation
Birke and Sarkar (2007), topic modeling Strzalkowski et al. (2013); Heintz et
al. (2013), dependency structures Jang et al. (2015), and semantic analysis
Hovy et al. (2013). In terms of generation, early systems have explored
grammar rules Gargett and Barnden (2013), while more recently, large language
models have greatly aided in metaphor generation. Most notably, Stowe et al.
(2021) generate metaphors by considering conceptual mappings between certain
domains and verbs. Chakrabarty et al. (2021) further build on this by creating
a parallel corpus of metaphors and training a large language model to perform
the generation.

We also note here that the two aforementioned studies already cover
personifications to a certain extent. However, these studies considered
personifications as subtypes of metaphors. Some of the methods used may not
generalize well to other types of personifications. Our study is the first to
focus specifically on generating personifications.

For generating similes, Chakrabarty et al. (2020) propose using style-transfer
models with COMET commonsense knowledge to generate similes. The study
similarly creates a parallel corpus and trains a seq2seq model to perform the
generation.

There is also a recent work by Keh et al. (2022) that uniquely investigates
the generation of tongue twisters using seq2seq and language models.

Personifications. There are currently few studies that specifically work on
personifications. Gao et al. (2018) detect personifications as a subtype of
metaphors, but not as its own figure of speech. Generation is largely
unexplored. We believe this is likely because personifications are generally
more difficult to define and categorize. Furthermore, because several sources
simplify personifications to fall under metaphors Stowe et al. (2021);
Chakrabarty et al. (2021), there is also a lack of personification-specific
datasets.

Constrained Text Generation. There is also a body of work exploring the family
of more general constrained text generation tasks. Gangal et al. (2022)
investigate NAREOR, or narrative ordering, which rewrites stories in distinct
narrative orders while preserving the underlying plot. Miao et al. (2019) show
gains on several tasks through determining Levenshtein edits per generation
step using Metropolis-Hastings sampling. Feng et al. (2019) propose Semantic
Text Exchange to modify the topic-level semantics of a piece of text.

Lin et al. (2020) propose CommonGen, a generative commonsense reasoning task
based on concept-to-text generation. Works investigating this task include
EKI-BART Fan et al. (2020) and KG-BART Liu et al. (2021), which use external
knowledge to enhance performance on CommonGen. SAPPHIRE Feng et al. (2021b)
uses the data itself and the model’s own generations to improve CommonGen
performance, while VisCTG Feng et al. (2022) uses per-example visual
grounding.

##  6 Conclusion and Future Work

In this paper, we explored the task of personification generation. We curated
a dataset of personifications and proposed the PINEAPPLE method to
automatically de-personify text. Using our parallel corpus, PersonifCorp, we
trained a seq2seq model (BART) to generate creative personifications. Through
automatic, human, and qualitative evaluation, we demonstrated that these
personifications make sentences more interesting and enhance the text’s
overall appeal. Our finetuned model successfully does this while maintaining a
high level of fluency and meaning perservation.

Some weaknesses of our model include failing to personify more complex
sentence structures, and occasionally failing to preserve the exact meaning of
the original sentence. We also believe that our model still has room to grow
in terms of the diversity of personifications generated. Further, we can
explore unsupervised style transfer methods Yang et al. (2018); Malmi et al.
(2020); Krishna et al. (2020), where we regard the personificationhood of a
sentence as a kind of style. We can also investigate data augmentation methods
Feng et al. (2021a, 2020); Dhole et al. (2021) to further expand our dataset.
Another promising direction would be to explore ways to acquire more control
over which parts of the sentence are personified or what types of
personifications are generated, or to apply this to make dialogue agents more
interesting, e.g. by giving them more personality Li et al. (2020).

## Ethics Statement

Our human and automatic evaluations (see §3.2) are done over content either
directly sourced from, or generated by publically available, off-the-shelf
pretrained models trained either on already existing, publicly available
datasets, or datasets further derived by post-processing the same — as further
described in Datasets (see §2 for more).

We do collect human evaluation ratings using crowd-sourcing, specifically
through AMT and in-person annotation. However, we neither solicit, record, nor
request any kind of personal or identity information from the annotators. Our
AMT annotation was conducted in a manner consistent with terms of use of any
sources and intellectual property and privacy rights of AMT crowd workers.
Crowdworkers were fairly compensated: $1.12 per fluency + appropriateness +
meaning preservation evaluation HIT, and $0.56 per personificationhood
evaluation HIT, for roughly 6 min (first) and 2 min (latter) tasks,
respectively. This is at least 1.5-2 times the minimum U.S.A. wage of $7.25
per hour ($0.725 per 6 minutes and $0.25 per 2 minutes).

We primarily perform experiments on personification in English Bender and
Friedman (2018).

NLG models are known to suffer from biases learnable from training or
finetuning on data, such as gender bias Dinan et al. (2020). However, our work
and contribution does not present or release any completely new model
architechtures, and is primarily concerned with more careful adaptation and
finetuning of existing pretrained models for a particular class of figurative
construct (i.e. personification). The frailties, vulnerabilities, and
potential dangers of these models have been well researched and documented,
and a specific re-investigation would be repetitive and beyond the scope and
space constraints of this paper.

We do not foresee any explicit way that malicious actors could specifically
misuse fintuned models that could be trained on our data, beyond the well-
researched, aforementioned misuse that is possible in general with their
instantiation for any transduction task or dataset (e.g. summarization).

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##  Appendix A Appendix A: De-Personification Pipeline

###  A.1 Dependency Tree Merging Example

Figure 4 contains an example of the merging process that was described in the
topic-attribute extraction step in §2. As outlined in §2, edge relations to
iteratively merge are negation modifiers, possession modifiers, nominal
modifiers, adjectival complements, and objects of prepositions, as well as
words tagged as determiners and parts of compound phrases. The priority order
for merging is as follows: 1) compound phrases, 2) nominal modifiers, 3)
possession modifiers, 4) negation modifiers, 5) determiners, 6) objects of
prepositions, 7) adjectival complements.

Figure 4: Step-by-step example of the merging process for topic-attribute
identification.

###  A.2 Human-Related Words

In §2, we defined the IsAPerson animacy metric as the average of the IsA
scores between the topic and various words that are very closely related to
‘human’. We called this set the humanset. The words contained in humanset are
as follows: {“person”, “human”, “man”, “woman”, “human being”, “boy”, “girl”}.

These words were empirically selected by considering a list of synonyms of the
word ‘person’ and checking the IsA relation COMET scores with the word
‘human’. All of the above words have IsA scores with ‘person’ of less than
5.10.

###  A.3 Parameters and Thresholds

IsAPerson Threshold.  For the IsAPerson animacy score, we use a threshold of
7.0. IsAPerson scores $<7.0$ are considered animate, while scores $\geq 7.0$
are considered inanimate. This threshold was selected empirically using words
known to be animate and words known to be inanimate. Words tested include
“she” (5.31), “person” (6.41), “moon” (8.743), “opportunity” (9.488), “stars”
(8.717), “joe” (5.804), “jane” (4.976), “the police officer” (6.462), “my
friend” (6.805), “my new iphone” (10.055). From these observations, we observe
that all animate words have an IsAPerson score of $<7.0$, while all inanimate
objects have a score of $\geq 7.0$. We hence conclude that $7.0$ is a suitable
threshold.

Candidate Selection Composite Score Parameters.  For the $\alpha,\beta,\gamma$
used in the composite score for candidate selection, we use values of
$\alpha=1,\beta=1,\gamma=1$. This was selected for two reasons. First, all of
the score values had largely similar scales (logarithmic), so setting
$\alpha,\beta,\gamma$ to a larger value like 2 or 3 would disproportionately
favor a certain metric, which is not what we desire. Second, we experimented
with using values such as 0.8, 1.2, and 1.5, but the generated de-
personifications were either very similar or slightly worse than the default
setting of $\alpha=1,\beta=1,\gamma=1$. A possible future direction would be
to explore possible values of $\alpha,\beta,\gamma$ more thoroughly, but for
this dataset, we stick to the simple case of $\alpha=1,\beta=1,\gamma=1$.

##  Appendix B Appendix C: Evaluation Details

###  B.1 Human Evaluation Setup

A total of 20 unique AMT annotators participated in the study for fluency,
appropriateness, and meaning preservation, each performing 4.0 HITs on
average. Annotators were compensated 1.12$ per HIT, each of which was designed
to take <6 mins on average.

22 unique AMT annotators participated in the second, separate study for
personificationhood, each performing 4.36 HITs on average. Annotators were
compensated 0.56$ per HIT, each of which was designed to take <2 mins on
average.

For the interestingness study, the details regarding annotator background and
selection can be found in §3.2.2 and Appendix B.3.

The html templates including instructions, questions and other study details
corresponding to both these AMT studies can be found in the templates/
subfolder of our code submission zip, with the names fluency_appropriateness_
meaningPreservation.html and personificationhood.html respectively.

###  B.2 Inter-Annotator Agreement Scores

Metric |  | Spearman
---
Correlation
Krippendorff $\alpha$
Personificationhood | 0.0934 | 0.0250
Appropriateness | 0.1660 | 0.1778
Fluency | 0.0050 | 0.0942
Interestingness | 0.6160 | 0.5898
Meaning Preservation | 0.0389 | 0.2558
Table 5: Inter-annotator agreement scores.

Each generated input instance and its respective model outputs are labelled by
two distinct annotators. To measure inter-annotator agreement, we use Spearman
correlation and Krippendorff $\alpha$, as reported in Table 5.

To get the Spearman correlation point value for a given aspect and test
instance, we compute mean pairwise Spearman correlation between the aspect
values assigned to the corresponding model outputs by every pair of
annotators. Specifically, we use the scipy.stats implementation to compute
this.
101010https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html

For Krippendorff $\alpha$, we treat each human evaluation aspect as an ordinal
quantity. Specifically, we use the implementation provided by the python
library krippendorff 0.5.1.111111https://pypi.org/project/krippendorff/

###  B.3 English Assessment Test for Annotators

From the native English-speaking university student annotators who enrolled to
participate in our Interestingness study, we first elicited answers to an
English assessment test, as mentioned in §3.2.2.

The assessment test comprised of 12 questions spanning multiple question types
testing the examinee’s grasp of the use and distinction between various
figures of speech, basic literary general knowledge, and verbal reasoning
skills. A spreadsheet file containing this test can be found with the name
LiteratureTest.xlsx under the Templates/ subfolder of our code submission .zip
file.

The final annotators used for our interestingness study were chosen from those
who got 11 or more of the 12 questions on the English assessment test correct,
hence scoring at least $85\%$ on the test.

##  Appendix C Appendix B: Implementation Details

The BART-base model was trained using a learning rate of 2e-5. This was by
conducting a hyperparameter search over the values {1e-6, 5e-6, 1e-5, 2e-5,
5e-5, 1e-4} and selecting the model/epoch based on lowest validation loss. The
same process was done to select a batch size of 4 using a hyperparameter
search over values {2,4,8,16}. Training was done for 20 epochs and 400 warmup
steps. The Adam optimizer was used, and inputs were truncated to a maximum
length of 64 tokens (using BART’s subword tokenization).

Training was done on Google Colaboratory environments using V100 GPUs. For the
BART-base model, a single training loop of 20 epochs takes approximately 10
minutes to complete.

##  Appendix D Appendix D: Additional Examples

Table 6 is an extension of Table 4 and contains additional qualitative
examples of the generations.

Method |  Text
---|---
Literal Input |  The news hit me hard. (ex.7)
Human Ref |  The news punched me hard.
COMET |  The news report event late me hard.
BL-BART |  The news hit me hard.
PA-BART |  The news pounded me hard.
Literal Input |  Panic is sweeping through the streets contagiously. (ex.8)
Human Ref |  Panic is driving around the streets contagiously.
COMET |  Panic is result through the streets contagiously.
BL-BART |  Panic is running through the streets contagiously.
PA-BART |  Panic runs through the streets contagiously.
Literal Input |  In the dead of summer, in Ohio, the clothes felt hot and humid. (ex.9)
Human Ref |  In the dead of summer, in Ohio, the clothes seemed to be sweating themselves.
COMET |  In the dead of summer, in Ohio, the clothes fit and humid.
BL-BART |  In the dead of summer, in Ohio, the clothes we wear can be hot and humid.
PA-BART |  In the dead of summer, in Ohio, the clothes complained and humid.
Literal Input |  Streaks of electricity zipped through the air, swirling around and around dangerous (ex.10)
Human Ref |  Streaks of electricity cackled through the air, swirling around and around dangerous.
COMET |  Streaks of electricity stain carpet through the air, swirling around and around dangerous.
BL-BART |  Streaks of electricity were shooting through the air, swirling around and around the building.
PA-BART |  Streaks of electricity danced around and around dangerous.
Literal Input |  The sun is nowhere to be found, and the streets are eerily quiet (ex.11)
Human Ref |  The sun seems to be on leave, and the streets are eerily quiet.
COMET |  The sun down, and the streets full of traffic.
BL-BART |  The sun, and the streets.
PA-BART |  The sun refuses to be found, and the streets are polite
Literal Input |  The wind blew through me fast. (ex.12)
Human Ref |  The wind stampeded through me fast.
COMET |  The wind howl through me fast.
BL-BART |  The wind was going through me fast.
PA-BART |  The wind ran me fast.

Table 6: Additional qualitative examples for personification outputs: literal
input, human writing, COMET, BL-BART, and PA-BART.