# MERMAID: Metaphor Generation with Symbolism
and Discriminative Decoding

Tuhin Chakrabarty1 Xurui Zhang3, Smaranda Muresan1,4 and Nanyun Peng2
1Department of Computer Science, Columbia University
2 Computer Science Department, University of California, Los Angeles,
3Tsinghua University, 4Data Science Institute, Columbia University
{tuhin.chakr<EMAIL_ADDRESS>
<EMAIL_ADDRESS><EMAIL_ADDRESS>
###### Abstract

Generating metaphors is a challenging task as it requires a proper
understanding of abstract concepts, making connections between unrelated
concepts, and deviating from the literal meaning. In this paper, we aim to
generate a metaphoric sentence given a literal expression by replacing
relevant verbs. Based on a theoretically-grounded connection between metaphors
and symbols, we propose a method to automatically construct a parallel corpus
by transforming a large number of metaphorical sentences from the Gutenberg
Poetry corpus Jacobs (2018) to their literal counterpart using recent advances
in masked language modeling coupled with commonsense inference. For the
generation task, we incorporate a metaphor discriminator to guide the decoding
of a sequence to sequence model fine-tuned on our parallel data to generate
high quality metaphors. Human evaluation on an independent test set of literal
statements shows that our best model generates metaphors better than three
well-crafted baselines 66% of the time on average. Moreover, a task-based
evaluation shows that human-written poems enhanced with metaphors proposed by
our model are preferred 68% of the time compared to poems without metaphors.

##  1 Introduction

Czech novelist Milan Kundera in his book “The unbearable lightness of being"
said

“Metaphors are not to be trifled with. A single metaphor can give birth to
love."

Metaphors allow us to communicate not just information, but also feelings and
complex attitudes Veale et al. (2016). While most computational work has
focused on metaphor detection Gao et al. (2018); Stowe et al. (2019); Shutova
et al. (2010); Tsvetkov et al. (2014); Veale et al. (2016); Stowe and Palmer
(2018), research on metaphor generation is under-explored Yu and Wan (2019);
Stowe et al. (2020). Generating metaphors could impact many downstream
applications such as creative writing assistance, literary or poetic content
creation.

Literal Input1 |  | The wildfire spread through the forest
---
at an amazing speed.
GenMetaphor1 |  | The wildfire danced through the forest
---
at an amazing speed.
Literal Input2 |  | The window panes were rattling as the
---
wind blew through them
GenMetaphor2 |  | The window panes were trembling as
---
the wind blew through them
Table 1: Examples of two generated metaphors GenMetaphor1 and GenMetaphor2 by
our best model mermaid from their literal inputs.

Relevant statistics demonstrate that the most frequent type of metaphor is
expressed by verbs Steen (2010); Martin (2006). We therefore focus on the task
of generating a metaphor starting from a literal utterance Stowe et al.
(2020), where we transform a literal verb to a metaphorical verb. Table 1
shows examples of literal sentences and the generated metaphors.

To tackle the metaphor generation problem we need to address three challenges:
1) the lack of training data that consists of pairs of literal utterances and
their equivalent metaphorical version in order to train a supervised model; 2)
ensuring that amongst the seemingly endless variety of metaphoric expressions
the generated metaphor can fairly consistently capture the same general
meaning as the literal one, with a wide variety of lexical variation; and 3)
computationally overcome the innate tendency of generative language models to
produce literal text over metaphorical one.

In an attempt to address all these challenges, we introduce our approach for
metaphor generation called mermaid (MEtaphor geneRation with syMbolism And
dIscriminative Decoding), making the following contributions:

  * •

A method to automatically construct a corpus that contains 93,498 parallel
[literal sentence, metaphorical sentence] pairs by leveraging the
theoretically-grounded relation between metaphor and symbols. Barsalou et al.
(1999) showed how perceptual symbols arising from perception are used in
conceptual tasks such as representing propositions and abstract concepts.
Philosopher Susanne Langer in her essay “Expressiveness and Symbolism” stated
“A metaphor is not language, it is an idea expressed by language, an idea that
in its turn functions as a symbol to express something”. Our approach has two
steps: 1) identify a set of sentences that contains _metaphorical verbs_ from
an online poetry corpus; 2) convert these metaphorical sentences to their
literal versions using Masked Language Models and structured common sense
knowledge achieved from COMET Bosselut et al. (2019), a language model fine-
tuned on ConceptNet Speer et al. (2017). For the later, we exploit the
SymbolOf relation to make sure the generated sentence that contains the
literal sense of the verb has the same symbol as the metaphorical sentence.
For example, for the metaphorical sentence “The turbulent feelings that surged
through his soul" our method will generate “The turbulent feelings that
continued through his soul" maintaining the common symbolic meaning of (love,
loss, despair, sorrow, loneliness) between the two (Section 2).

  * •

Use of a metaphor discriminator to guide the decoding of a sequence-to-
sequence model fine-tuned on our parallel data to generate high quality
metaphors. Our system mermaid, _fine-tunes_ BART Lewis et al. (2019) – a state
of the art pre-trained denoising autoencoder built with a sequence to sequence
model, on our _automatically collected parallel corpus_ of [literal sentence,
metaphorical sentence] pairs (Sec. 3.1) to generate metaphors. A
discriminative model trained in identifying metaphors is further used to
complement our generator and guide the decoding process to improve the
generated output (Sec. 3.2). Human evaluations show that this approach
generates metaphors that are better than two literary experts 21% of the time
on average, better 81% of the time than two well-crafted baselines, and better
36% of the time than fine-tuned BART Lewis et al. (2019) (Section 5).

  * •

A task-based evaluation: improving the quality of human written poems.
Evaluation via Amazon Mechanical Turk shows that poems enhanced with metaphors
generated by mermaid are preferred by Turkers 68% of the times compared to
poems without metaphors, which are preferred 32% of the times (Section 6).
Our code, data and models can be found at
https://github.com/tuhinjubcse/MetaphorGenNAACL2021

##  2 Dataset Creation with MLM and Symbolism

Figure 1:  A schematic illustration of our system, which shows the data
creation and training process where we use MLM along with COMET to transform
an original metaphorical input to a literal output evoking similar symbolic
meaning and use them to fine-tune BART.

Datasets for metaphors are scarce. To our knowledge, there is no large scale
parallel corpora containing literal and metaphoric paraphrases. The closest
and most useful work is that of Mohammad et al. (2016). However the size of
this data-set is small: 171 instances, which is not sufficient to train deep
learning models. Recently, Stowe et al. (2020) rely on available metaphor
detection datasets to generate metaphors by a metaphor-masking framework,
where they replace metaphoric words in the input texts with metaphor masks (a
unique “metaphor” token), hiding the lexical item. This creates artificial
parallel training data: the input is the masked text, with the hidden
metaphorical word, and the output is the original text (e.g., The war [MASK]
many people $\rightarrow$ The war uprooted many people). The major issue with
such masking strategy is that it ignores the semantic mapping between the
literal verb and the metaphorical verb. Moreover, there are only 11,593 such
parallel instances, still too small to train a neural model. The lack of
semantic mapping between the artificial parallel training data samples,
coupled with limited size thus affects the lexical diversity and meaning
preservation of generated metaphors at test time. In light of these
challenges, we propose to compose a large-scale parallel corpora with literal
and metaphorical sentence pairs to learn the semantic mappings. We start with
collecting a large-scale corpora of metaphorical sentences (Section 2.1) and
leverage masked language model and symbolism-relevant common sense knowledge
to create literal version for each metaphorical sentence (Section 2.2).

###  2.1 Metaphor Dataset Collection

Metaphors are frequently used in Poetry to explain and elucidate emotions,
feelings, relationships and other elements that could not be described in
ordinary language. We use this intuition to identify a naturally occurring
poetry corpus that contains metaphors called Gutenberg Poetry Corpus Jacobs
(2018).111https://github.com/aparrish/gutenberg-poetry-corpus The corpus
contains 3,085,117 lines of poetry extracted from hundreds of books. Not every
sentence in the corpus contains a metaphorical verb. So as a first step, we
identify and filter sentences containing a metaphorical verb.

We build a classifier by fine-tuning BERT Devlin et al. (2018) on a metaphor
detection corpus VU Amsterdam Steen (2010). Since our work is focused on
verbs, we only do token classification and calculate loss for verbs. Figure 2
illustrates the BERT-based token-level classifier. The classification accuracy
on test set is 74.7%, which is on par with most state of art methods.

Using the metaphor detection model, we identify 622,248 ($20.2\%$) sentences
predicted by our model as containing a metaphoric verb. Considering the
classifier can introduce noise as the accuracy of the metaphor detection model
is far from oracle $100\%$, we only retain sentences which are predicted by
our model with a confidence score of $95\%$ (i.e., prediction probability
0.95). This results in a total number of 518,865 ($16.8\%$) metaphorical
sentences.

Figure 2: BERT-base-cased model to identify metaphoric verbs, where $v_{1}$ and $v_{2}$ represent the verbs in a sentence. (M) denotes softmax probabality of a verb being metaphorical, while (L) denotes it literal softmax probability. Input |  | The turbulent feelings that surged
---
through his soul .
Masked |  | The turbulent feelings that [MASK]
---
through his soul .
Ranked by MLM Prob |  | (‘tore’, 0.11), (‘ran’, 0.10), (‘ripped’, 0.09)
---
, (‘flowed’, 0.03), (‘rushed’, 0.01), ….. ,
(‘eased’, 0.01),…. , (‘continued’, 0.0005),…
Ranked by Meta Prob |  | (‘eased’, 0.12), (‘continued’,0.0008), (‘spread’,
---
0.0004), (‘kicked’, 0.99) ,(‘punched’,
0.99),…..,(‘screamed’, 0.99),…..
Table 2: Table showing a metaphorical sentence (Row1) where the metaphorical
verb surge is masked (Row2). Row3 shows predicted tokens ranked by default LM
probability. Row4 shows predicted tokens ranked by metaphoricity scores obtain
from model described in 2.1. Lower scores means more literal.

###  2.2 Metaphoric to Literal Transformation with Symbolism

After identifying high quality metaphorical sentences, we want to obtain their
literal counterparts to create a parallel training data. Masked language
models like BERT Devlin et al. (2018), or roBERTa Liu et al. (2019) can be
used for fill-in-the-blank tasks, where the model uses the context words
surrounding a masked token to predict the masked word. We borrow this
framework to mask the metaphorical verb (Table 2 Row1 vs Row2) from a sentence
and use BERT-base-cased model to obtain the top $200$ candidate verbs to
replace the metaphorical one to generate literal sentences (Table 2 Row3).
There are two main issues in solely relying on MLM predicted verbs: 1) they
are not necessarily literal in nature; 2) after replacing the default MLM
predicted verb, the metaphorical sentence and the new sentence with the
replaced verb might be semantically dissimilar.

####  2.2.1 Ensuring Literal Sense

Even though our inductive biases tell us that the chance of a predicted token
having a literal sense is higher than having a metaphorical one, this cannot
be assumed. To filter only literal candidate verbs we re-rank the MLM
predicted mask tokens based on literal scores obtained from 2.1 since the
model can predict the softmax probability of a verb in a sentence being either
literal or metaphorical (Table 2 Row4).

####  2.2.2 Ensuring Meaning Preservation

While we can potentially pair the sentence with the top most literal ranked
verb with the input sentence containing the metaphorical verb, they might
symbolically or semantically represent different abstract concepts. For
example, in Table 3, after replacing the metaphorical verb “surge" with the
top most literal verb “eased", the sentence “The turbulent feelings that eased
through his soul" evoke a different symbolic meaning of
peace,love,happiness,joy & hope in comparison to the input containing the
metaphorical verb, which evokes a symbolic meaning of love, loss, despair,
sorrow & loneliness. To tackle this problem we ensure that the transformed
literal output represents the same symbolic meaning as the metaphorical input.

To generate the common sense SYMBOL that is implied by the literal or
metaphorical sentences, we feed the sentences as input to COMET Bosselut et
al. (2019) and restrict it to return top-5 beams. COMET is an adapted
knowledge model pre-trained on
ConceptNet.222https://mosaickg.apps.allenai.org/comet_conceptnet Our work only
leverages the SymbolOf relation from COMET.

Meta Input |  | The turbulent feelings that surged
---
through his soul .
| Inp Symbol
---
love, loss, despair, sorrow, loneliness
Lit Output1 |  | The turbulent feelings that
---
eased through his soul .
✗
Symbol | peace,love,happiness,joy,hope
Lit Output2 |  | The turbulent feelings that
---
continued through his soul .
✓
Symbol | love, loss, despair, sorrow, loneliness
Table 3: Table showing input metaphorical sentence and literal outputs along
with the associated symbolic meaning obtained from COMET Bosselut et al.
(2019). Lit Output1 is an incorrect candidate since the symbolic meanings are
divergent.

We now need a method to combine information from MLM and symbolic knowledge
obtained from COMET described above. To do this, we filter candidates from MLM
token predictions based on the symbolic meaning overlap between the
metaphorical input and literal output first. To ensure that the quality is
high, we put a strict requirement that all the 5 symbolic beams (typically
words or short phrases) for the input metaphorical sentence should match all
the 5 symbolic beams for the output literal sentence. Between multiple literal
candidates all having beam overlap of 5, they are further ranked by reverse
metaphoricity (i.e., literal) scores. The top most candidate is returned
thereafter. We finally end up with 90,000 pairs for training and 3,498 pairs
for validation.

##  3 Metaphor Generation

Figure 3:  Schematic showing the decoding step where we use fine-tuned BART
along with a metaphor detecting discriminator to generate a metaphorical
sentence conditioned on a literal input

Our goal of generating metaphors can be broken down into two primary tasks: 1)
generating the appropriate substitutions for the literal verb while being
pertinent to the context; 2) ensuring that the generated utterances are
actually metaphorical.

###  3.1 Transfer Learning from BART

To achieve the first goal, we fine-tune BART Lewis et al. (2019), a pre-
trained conditional language model that combines bidirectional and auto-
regressive transformers, on the collected parallel corpora. Specifically, we
fine-tune BART by treating the literal input as encoder source and the
metaphorical output as the the decoder target (Figure 1). One issue of the
pre-trained language models is that they have a tendency to generate literal
tokens over metaphorical ones. To overcome this, we introduce a rescoring
model during the decoding process to favor more metaphorical verbs. The
rescoring model is inspired by Holtzman et al. (2018); Goldfarb-Tarrant et al.
(2020) and detailed in the next section.

###  3.2 Discriminative Decoding

We have a base metaphor generation model $p(\textbf{z}|\textbf{x})$ which is
learned by fine-tuning BART Lewis et al. (2019) on pairs of literal ($x$) and
metaphorical ($z$) sentences. We propose to modify the decoding objective to
incorporate a Metaphor detection rescoring model $a$ and re-rank the base, or
“naive" BART generated hypotheses, bringing the metaphoric representation
closer to the rescoring model’s specialty and desirable attribute. The
modified decoding objective becomes:

$\displaystyle f_{\lambda}({\bf x,z})=\sum_{i}^{m}-\log p(z|z<i,{\bf
x})+\lambda a({\bf x},z_{i...m})$ (1)

where $\lambda$ is a weight of the score given by $a$.

##### Implementation Details

We use top-k sampling strategy Fan et al. (2018) (k=5) to generate metaphors
conditioned on a literal input. Our rescoring model $a$ is a RoBERTa model
fine-tuned on a combined dataset of Steen (2010); Beigman Klebanov et al.
(2018) to classify sentences as literal or metaphorical based on whether there
exists a metaphorical verb. It is a sentence level task where the model
predicts a sentence as literal or metaphorical. We down-sample the data to
maintain a ratio of ($1:1$) between two classes and use $90\%$ of the data to
train and $10\%$ for validation. We achieve a considerably decent validation
accuracy of $83\%$. We manually tune $\lambda$ using grid search on a small
subset of 3,498 validation samples from our parallel automatic data and choose
the best value.

Figure 3 shows the process of re-ranking BART hypothesis using the
discriminator described above to generate novel metaphorical replacements for
literal verbs. All the hyper-parameters for data creation, fine-tuning and
discriminative decoding are exactly the same as mentioned in Appendix A.

The reason to use a separate discriminator for decoding instead of using the
same BERT based classifier used for parallel data creation, was to avoid
introducing dataset biases or spurious correlations. The BERT-based classifier
used for automatically creating the parallel dataset ideally has already
picked up salient metaphorical phenomena in the VUA dataset. To further guide
the decoding process, we hypothesize that a model trained on datasets not seen
during training would lead to better generalization. We experimented with
using the BERT model trained on VUA for rescoring, but the results were not
better.

##  4 Experimental Setup

To compare the quality of the generated metaphors, we benchmark our mermaid
model against human performance (i.e., the two creative writing experts HUMAN1
(a novelist) & HUMAN2 (a poet) who are not the authors of the paper) (Section
4.2) and three baseline systems described below.

###  4.1 Baseline Systems

Lexical Replacement (LEXREP): We use the same idea as our data creation
process (Section 2.2). We use our model described in Section 2.1 to re-rank
the predicted tokens from a mask language model based on metaphoricity scores.
We filter the top 25 ranked metaphorical candidates and further rerank them
based on symbolic meaning overlap with the literal meaning using COMET
Bosselut et al. (2019) and replace the literal verb with the top scoring
candidate.

Metaphor Masking (META_M): We use the metaphor masking model proposed by Stowe
et al. (2020) where the language model learns to replace a masked verb with a
metaphor. They train a seq2seq model with the encoder input of the format (The
tax cut [MASK] the economy) and the decoder output being the actual
metaphorical sentence (The tax cut lifted the economy). During inference, they
mask the literal verb and expect the language model to infill a metaphorical
verb.

BART: We use generations from a BART model fine-tuned on our automatically
created data without the discriminative decoding. This helps us gauge the
effect of transfer learning from a large generative pre-trained model, which
also accounts for context unlike the retrieval based methods.

###  4.2 Test Data

To measure the effectiveness of our approach, we need to evaluate our model on
a dataset that is independent of our automatically created parallel data and
that is diverse across various domains, genres and types. Hence we rely on
test data from multiple sources. As our first source, we randomly sample
literal and metaphorical sentences with high confidence ($>0.7$) and unique
verbs from the existing dataset introduced by Mohammad et al. (2016). For the
metaphorical sentences from Mohammad et al. (2016) we convert them to their
literal equivalent the same way as discussed in Section 2.2 without the use of
COMET as we do not need it. To ensure diversity in genre, as our second source
we scrape WritingPrompt and Ocpoetry subreddits for sentences with length up
to 12 words, which are _literal_ in nature based on prediction from our model
described in Section 2.1. We collate 500 such sentences combined from all
sources and randomly sample 150 literal utterance for evaluation.

We use two literary experts (not authors of this paper) — a student in
computer science who is also a poet, and a student in comparative literature
who is the author of a novel — to write corresponding metaphors for each of
these 150 inputs for evaluation and comparison.

###  4.3 Evaluation Criteria

##### Automatic evaluation.

One important aspect in evaluating the quality of the generated metaphors is
whether they are faithful to the input: while we change literal sentences to
metaphorical ones, it should still maintain the same denotation as the input.
To this end, we calculate the Semantic Similarity between the metaphorical
output and the input using sentence-BERT (SBERT) Reimers and Gurevych (2019).
We also calculate corpus-level BLEU-2 Papineni et al. (2002) and BERTScore
Zhang et al. (2019) with human written references.

##### Human evaluation.

Since automatic evaluation is known to have significant limitations for
creative generation Novikova et al. (2017), we further conduct human
evaluation on a total of 900 utterances, 600 generated from 4 systems and 300
generated by the two human experts. We propose a set of four criteria to
evaluate the generated output: (1) Fluency (Flu) (“How fluent, grammatical,
well formed and easy to understand are the generated utterances?”), (2)
Meaning (Mea) (“Are the input and the output referring or meaning the same
thing?") (3) Creativity (Crea) (“How creative are the generated utterances?”),
and (4) Metaphoricity (Meta) (“How metaphoric are the generated utterances”).
The human evaluation is done on the Amazon Mechanical Turk platform. Each
Turker was given a literal input and 6 metaphorical outputs (4 from system
outputs – 3 baselines and our proposed system mermaid, and 2 from humans) at a
time, with the metaphorical outputs randomly shuffled to avoid potential
biases. Turkers were instructed to evaluate the quality of the metaphorical
sentences with respect to the input and not in isolation. As we evaluate on
four dimensions for 900 utterances, we have a total of 3600 evaluations. Each
criteria was rated on a likert scale from 1 (not at all) to 5 (very). Each
group of utterances was rated by three separate Turkers, resulted in 42, 48,
44 and 53 Turkers for the four evaluation tasks respectively. We pay them at a
rate of $15 per hour.

System | Similarity $\uparrow$ | BLEU-2$\uparrow$ | BertScore$\uparrow$
---|---|---|---
LEXREP | 79.6 | 68.7 | 0.56
META_M | 73.2 | 61.0 | 0.62
BART | 83.6 | 65.0 | 0.65
mermaid | 85.0 | 66.7 | 0.71
HUMAN1 | 86.6 | - | -
HUMAN2 | 84.2 | - | -
Table 4: Automatic evaluation results on test set where mermaid significantly
outperforms other automatic methods for 2 out of 3 metrics ($p<.001)$
according to approximate randomization test). BLEU-2 and BertScore is
calculated w.r.t to Human references (HUMAN1 & HUMAN2). Corpus level BLEU-2
and Semantic Similarity are in range of (0-100) while BertScore is in range
(0-1)

##  5 Results

System |  Flu |  Mea |  Crea |  Meta
---|---|---|---|---
HUMAN1 |  3.83 |  3.77 |  4.02 |  3.52
HUMAN2 |  3.29 |  3.43 |  3.58 |  3.16
LEXREP |  2.21 |  2.59 |  2.16 |  1.98
META_M |  2.10 |  1.91 |  2.00 |  1.89
BART |  3.33 |  3.08 |  3.16 |  2.85
mermaid |  3.46 |  3.35 |  3.50 |  3.07
Table 5: Human evaluation on four criteria of metaphors quality for systems and humans generated metaphors. We show average scores on a likert scale of 1-5 where 1 denotes the worst and 5 be the best. Boldface denotes the best results overall and underscore denotes the best among computational models. Literal | System |  Metaphor | Flu | Mea | Crea | Meta
---|---|---|---|---|---|---
The scream filled the night | HUMAN1 |  The scream pierced the night | 4.3 | 5.0 | 3.7 | 4.0
HUMAN2 |  The scream covered the night | 2.7 | 4.0 | 3.0 | 3.0
LEXREP |  The scream held the night | 1.7 | 3.7 | 2.0 | 1.7
META_M |  The scream opened the night | 1.0 | 1.0 | 1.0 | 1.0
BART |  The scream filled the night | 2.3 | 1.0 | 2.3 | 1.0
mermaid |  The scream pierced the night | 4.3 | 5.0 | 3.7 | 4.0
The wildfire spread through the forest at an amazing speed | HUMAN1 |  The wildfire ravaged through the forest at an amazing speed | 4.7 | 4.3 | 4.0 | 3.0
HUMAN2 |  The wildfire leapt through the forest at an amazing speed | 3.7 | 3.0 | 5.0 | 3.7
LEXREP |  The wildfire saw through the forest at an amazing speed | 1.3 | 1.0 | 2.7 | 3.3
META_M |  The wildfire grows through the forest at an amazing speed | 3.7 | 2.7 | 2.7 | 4.0
BART |  The wildfire swept through the forest at an amazing speed | 4.0 | 3.7 | 4.7 | 4.0
mermaid |  The wildfire danced through the forest at an amazing speed | 3.0 | 4.0 | 4.0 | 3.7
My heart beats when he walks in the room | HUMAN1 |  My heart skips when he walks in the room | 4.7 | 5.0 | 4.0 | 4.3
HUMAN2 |  My heart sings when he walks in the room | 5.0 | 4.3 | 3.7 | 3.3
LEXREP |  My heart made when he walks in the room | 1.0 | 1.0 | 1.0 | 1.0
META_M |  My heart came when he walks in the room | 1.7 | 1.0 | 1.3 | 1.3
BART |  My heart sings when he walks in the room | 5.0 | 4.3 | 3.7 | 3.7
mermaid |  My heart jumps when he walks in the room | 4.7 | 4.7 | 4.3 | 4.0
After a glass of wine, he relaxed up a bit | HUMAN1 |  After a glass of wine, he loosened up a bit | 4.7 | 5.0 | 5.0 | 4.0
HUMAN2 |  After a glass of wine, he unfurled up a bit | 2.0 | 5.0 | 2.0 | 3.7
LEXREP |  After a glass of wine, he followed up a bit | 3.7 | 1.0 | 2.7 | 1.7
META_M |  After a glass of he touched up a bit | 1.3 | 1.0 | 1.7 | 2.0
BART |  After a glass of wine, he dried up a bit | 2.7 | 1.0 | 2.3 | 2.0
mermaid |  After a glass of wine, he loosened up a bit | 4.3 | 5.0 | 5.0 | 3.7
The tax cut will help the economy | HUMAN1 |  The tax cut will uplift the economy | 4.7 | 5.0 | 4.7 | 4.0
HUMAN2 |  The tax cut will fertilize the economy | 4.0 | 4.3 | 4.3 | 3.7
LEXREP |  The tax cut will bring the economy | 1.7 | 3.0 | 2.7 | 1.7
META_M |  The tax cut will prevent the economy | 1.7 | 1.0 | 2.0 | 1.0
BART |  The tax cut will strengthen the economy | 5.0 | 5.0 | 4.3 | 3.7
mermaid |  The tax cut will stimulate the economy | 5.0 | 4.7 | 3.7 | 4.0
I tried to resolve things over between them | HUMAN1 |  I tried to tide things over between them | 4.3 | 3.0 | 3.7 | 4.3
HUMAN2 |  I tried to patch things over between them | 4.7 | 4.7 | 5.0 | 2.0
LEXREP |  I tried to push things over between them | 3.3 | 1.0 | 2.3 | 2.0
META_M |  I tried to make things over between them | 4.0 | 1.0 | 2.7 | 2.7
BART |  I tried to put things over between them | 4.7 | 2.0 | 3.0 | 2.7
mermaid |  I tried to smooth things over between them | 4.7 | 4.7 | 5.0 | 4.0
Table 6: Examples of generated outputs from different systems (with human
written metaphors as references). We show average scores (over three
annotators) on a 1-5 scale with 1 denotes the worst and 5 be the best. The
italics texts in the literal column represent the verb while those in Metaphor
column represents the generated metaphorical verb. Boldface indicates the best
results.

Based on the semantic similarity metric shown in column 1 of Table 4, our
system mermaid is better in preserving the meaning of the input than the other
baselines. As mentioned, we calculate BLEU-2 and BERTScore between system
outputs and human references. mermaid is better than the other baselines
according to BERTScore. In terms of BLEU-2, mermaid is second best.

Table 5 shows the average scores for the human evaluation on four metaphor
quality criteria for mermaid, the baselines, and human written metaphors on
the test set. The inter-annotator agreements computed using Krippendorff’s
alpha for Creativity, Meaning, Fluency and Metaphoricity are 0.44, 0.42, 0.68,
0.52 respectively. The results demonstrate that mermaid is significantly
better than the baselines on all four criteria ($p<.001$ according to
approximate randomization test).

Table 6 presents several generation outputs from different systems along with
human judgements on individual criteria. We observe that incorporating a
discriminator often guides our model to generate better metaphors than the
already strong baseline using BART. Finally, incorporating symbolic meaning in
data creation step helps our model to maintain the same meaning as the input.

##  6 Task Based Evaluation

Figure 4:  Percentage of Preference of Original Quatrains vs Quatrains
rewritten by mermaid

Metaphors are frequently used by creative writing practitioners, in particular
poets, to embellish their work. We posit that mermaid can be used to edit
literal sentences in poems to further enhance creativity. To test this
hypothesis, we first crawl original poems submitted by authors from the sub-
reddit OCPoetry. The poems are of variable lengths, so to ensure parity we
break them into Quatrains (four sentence stanza). We randomly sample 50 such
Quatrains containing at least one sentence with a literal verb in it. We use
our metaphor detection model (Section 2.1) to detect literal verbs.

We then select a sentence containing a literal verb from each Quatrain and use
mermaid to re-write it so that the resulting output is metaphorical. We ignore
common verbs like is,was,are,were,have,had. If there are more than one
sentence in Quatrain with literal verbs, we choose the sentence with a literal
verb that has the highest probability for being literal. For sentences with
multiple literal verbs, we choose the verb with highest literal probability.

| And the hills have a shimmer of light between,
---
And the valleys are covered with misty veils,
And ………,
…..
| And the hills have a shimmer of light between,
---
And the valleys are wrapped with misty veils,
And ………,
| Leaves on a maple, burst red with the shorter days;
---
Falling to the ground.
….
| Leaves on a maple, burgeoned red with the shorter days;
---
Falling to the ground.
….
Table 7: Example Quatrains from reddit where mermaid rewrites a sentence
containing a literal verb to make it metaphorical.

Our goal is to see if re-written poems are qualitatively better than the
original forms. To do this, we hire Turkers from Amazon Mechanical Turk and
present them with hits where the task is to choose the better version between
the original Quatrain and the re-written version. 15 Turkers were recruited
for the task. Each Quatrain was evaluated by 3 distinct Turkers. Table 7 shows
metaphorical transformations by a mermaid Figure 4 shows that poems rewritten
by mermaid were considered better by the Turkers.

##  7 Related Work

Most researchers focused on identification and interpretation of metaphor,
while metaphor generation is relatively under-studied.

###  7.1 Metaphor Detection

For metaphor detection, researchers focused on variety of features, including
unigrams, imageability, sensory features, WordNet, bag-of-words features
Klebanov et al. (2014); Tsvetkov et al. (2014); Shutova et al. (2016);
Tekiroğlu et al. (2015); Hovy et al. (2013); Köper and im Walde (2016).

With advent of deep learning approaches, Gao et al. (2018) used BiLSTM models
based on GloVe Pennington et al. (2014) and ELMo word vectors Peters et al.
(2018) to detect metaphoric verbs. Inspired by the linguistic theories, MIP
Semino et al. (2007); Steen (2010) and SPV Wilks (1975, 1978), Mao et al.
(2019) proposed two detection models consisting of BiLSTM with attention
mechanisms that relied on GloVe and ELMo embeddings. Recent work on metaphor
detection have also used pretrained language models Su et al. (2020); Gong et
al. (2020). While we focus on metaphor generation , we use Devlin et al.
(2018) to detect metaphoric verbs to create parallel data and Liu et al.
(2019) to rescore our generated hypothesis during decoding.

###  7.2 Metaphor Generation

Some early works made contributions to use template and heuristic-based
methods Abe et al. (2006); Terai and Nakagawa (2010) to generate “A is like B”
sentences, more popularly referred to as similes. Chakrabarty et al. (2020)
concentrated on simile generation, applying seq2seq model to paraphrase a
literal sentence into a simile. Other attempts learned from the mappings of
different domains and generated conceptual metaphors of pattern “A is B”
Hervás et al. (2007); Mason (2004); Gero and Chilton (2019). These works paid
attention to the relationship between nouns and concepts to create elementary
figurative expressions.

Recent metaphor generation works focus mainly on verbs. Yu and Wan (2019)
proposed an unsupervised metaphor extraction method, and developed a neural
generation model to generate metaphorical sentences from literal-metaphorical
verb pairs. They however do not focus on literal to metaphorical sentence
transfer , but generate a sentence given a metaphorical fit word. The closest
to our work is that of Stowe et al. (2020), who focus on building a seq2seq
model, using a special mask token to mask the metaphorical verbs as input, and
the original metaphorical sentences as output. However, this model face
challenges in transferring the literal sentences to metaphorical ones, while
maintaining the same meaning. We, on the contrary, focus on maintaining the
same meaning through parallel data creation focusing on symbolism.
Additionally, we incorporate a metaphor detection model as a discriminator to
improve decoding during generation.

##  8 Conclusion

We show how to transform literal sentences to metaphorical ones. We propose a
novel way of creating parallel corpora and an approach for generating
metaphors that benefits from transfer learning and discriminative decoding.
Human and automatic evaluations show that our best model is successful at
generating metaphors. We further show that leveraging symbolic meanings helps
us learn better abstract representations and better preservation of the
denotative meaning of the input. Future directions include learning diverse
conceptual metaphoric mapping using our parallel data and constraining our
metaphoric generations based on particular mapping.

##  9 Ethics

Our data is collected from Reddit and we understand and respect user privacy.
Our models are fine-tuned on sentence level data obtained from user posts.
These do not contain any explicit detail which leaks information about a users
name, health, negative financial status, racial or ethnic origin, religious or
philosophical affiliation or beliefs, sexual orientation, trade union
membership, alleged or actual commission of crime.

Second, although we use language models trained on data collected from the
Web, which have been shown to have issues with bias and abusive language Sheng
et al. (2019); Wallace et al. (2019), the inductive bias of our models should
limit inadvertent negative impacts. Unlike model variants such as GPT, BART is
a conditional language model, which provides more control of the generated
output. Furthermore, we specifically encode writing style from a poetic corpus
in our models and train on parallel data in the direction of literal to
metaphorical style. Open-sourcing this technology will help to generate
metaphoric text assisting creative writing practitioners or non native
language speakers to improve their writing. We do not envision any dual-use
that can cause harm for the use of our the metaphor generation system.

## References

  * Abe et al. (2006) Keiga Abe, Kayo Sakamoto, and Masanori Nakagawa. 2006.  A computational model of the metaphor generation process.  In _Proceedings of the Annual Meeting of the Cognitive Science Society_ , volume 28.
  * Barsalou et al. (1999) Lawrence W Barsalou et al. 1999.  Perceptual symbol systems.  _Behavioral and brain sciences_ , 22(4):577–660.
  * Beigman Klebanov et al. (2018) Beata Beigman Klebanov, Chee Wee (Ben) Leong, and Michael Flor. 2018.  A corpus of non-native written English annotated for metaphor.  In _Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)_ , pages 86–91, New Orleans, Louisiana. Association for Computational Linguistics.
  * Bosselut et al. (2019) Antoine Bosselut, Hannah Rashkin, Maarten Sap, Chaitanya Malaviya, Asli Celikyilmaz, and Yejin Choi. 2019.  COMET: Commonsense transformers for automatic knowledge graph construction.  In _Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics_ , pages 4762–4779, Florence, Italy. Association for Computational Linguistics.
  * Chakrabarty et al. (2020) Tuhin Chakrabarty, Smaranda Muresan, and Nanyun Peng. 2020.  Generating similes effortlessly like a pro: A style transfer approach for simile generation.  In _Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)_ , pages 6455–6469, Online. Association for Computational Linguistics.
  * Devlin et al. (2018) Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018.  Bert: Pre-training of deep bidirectional transformers for language understanding.  _arXiv preprint arXiv:1810.04805_.
  * Fan et al. (2018) Angela Fan, Mike Lewis, and Yann Dauphin. 2018.  Hierarchical neural story generation.  _arXiv preprint arXiv:1805.04833_.
  * Gao et al. (2018) Ge Gao, Eunsol Choi, Yejin Choi, and Luke Zettlemoyer. 2018.  Neural metaphor detection in context.  _arXiv preprint arXiv:1808.09653_.
  * Gero and Chilton (2019) Katy Ilonka Gero and Lydia B Chilton. 2019.  Metaphoria: An algorithmic companion for metaphor creation.  In _Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems_ , pages 1–12.
  * Goldfarb-Tarrant et al. (2020) Seraphina Goldfarb-Tarrant, Tuhin Chakrabarty, Ralph Weischedel, and Nanyun Peng. 2020.  Content planning for neural story generation with aristotelian rescoring.  _arXiv preprint arXiv:2009.09870_.
  * Gong et al. (2020) Hongyu Gong, Kshitij Gupta, Akriti Jain, and Suma Bhat. 2020.  Illinimet: Illinois system for metaphor detection with contextual and linguistic information.  In _Proceedings of the Second Workshop on Figurative Language Processing_ , pages 146–153.
  * Hervás et al. (2007) Raquel Hervás, Rui P Costa, Hugo Costa, Pablo Gervás, and Francisco C Pereira. 2007.  Enrichment of automatically generated texts using metaphor.  In _Mexican International Conference on Artificial Intelligence_ , pages 944–954. Springer.
  * Holtzman et al. (2018) Ari Holtzman, Jan Buys, Maxwell Forbes, Antoine Bosselut, David Golub, and Yejin Choi. 2018.  Learning to write with cooperative discriminators.  _arXiv preprint arXiv:1805.06087_.
  * Hovy et al. (2013) Dirk Hovy, Shashank Srivastava, Sujay Kumar Jauhar, Mrinmaya Sachan, Kartik Goyal, Huying Li, Whitney Sanders, and Eduard Hovy. 2013.  Identifying metaphorical word use with tree kernels.  In _Proceedings of the First Workshop on Metaphor in NLP_ , pages 52–57.
  * Jacobs (2018) Arthur M Jacobs. 2018.  The gutenberg english poetry corpus: exemplary quantitative narrative analyses.  _Frontiers in Digital Humanities_ , 5:5.
  * Klebanov et al. (2014) Beata Beigman Klebanov, Ben Leong, Michael Heilman, and Michael Flor. 2014.  Different texts, same metaphors: Unigrams and beyond.  In _Proceedings of the Second Workshop on Metaphor in NLP_ , pages 11–17.
  * Köper and im Walde (2016) Maximilian Köper and Sabine Schulte im Walde. 2016.  Distinguishing literal and non-literal usage of german particle verbs.  In _Proceedings of the 2016 conference of the north American chapter of the association for computational linguistics: Human language technologies_ , pages 353–362.
  * Lewis et al. (2019) Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. 2019.  Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension.  _arXiv preprint arXiv:1910.13461_.
  * Liu et al. (2019) Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019.  Roberta: A robustly optimized bert pretraining approach.  _arXiv preprint arXiv:1907.11692_.
  * Mao et al. (2019) Rui Mao, Chenghua Lin, and Frank Guerin. 2019.  End-to-end sequential metaphor identification inspired by linguistic theories.  In _Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics_ , pages 3888–3898.
  * Martin (2006) James H Martin. 2006.  A corpus-based analysis of context effects on metaphor comprehension.
  * Mason (2004) Zachary J Mason. 2004.  Cormet: a computational, corpus-based conventional metaphor extraction system.  _Computational linguistics_ , 30(1):23–44.
  * Mohammad et al. (2016) Saif Mohammad, Ekaterina Shutova, and Peter Turney. 2016.  Metaphor as a medium for emotion: An empirical study.  In _Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics_ , pages 23–33, Berlin, Germany. Association for Computational Linguistics.
  * Novikova et al. (2017) Jekaterina Novikova, Ondřej Dušek, Amanda Cercas Curry, and Verena Rieser. 2017.  Why we need new evaluation metrics for NLG.  In _Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing_ , pages 2241–2252, Copenhagen, Denmark. Association for Computational Linguistics.
  * Ott et al. (2019) Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, and Michael Auli. 2019.  fairseq: A fast, extensible toolkit for sequence modeling.  _arXiv preprint arXiv:1904.01038_.
  * Papineni et al. (2002) Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002.  Bleu: a method for automatic evaluation of machine translation.  In _Proceedings of the 40th annual meeting on association for computational linguistics_ , pages 311–318. Association for Computational Linguistics.
  * Pennington et al. (2014) Jeffrey Pennington, Richard Socher, and Christopher D Manning. 2014.  Glove: Global vectors for word representation.  In _Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP)_ , pages 1532–1543.
  * Peters et al. (2018) Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018.  Deep contextualized word representations.  _arXiv preprint arXiv:1802.05365_.
  * Reimers and Gurevych (2019) Nils Reimers and Iryna Gurevych. 2019.  Sentence-bert: Sentence embeddings using siamese bert-networks.  _arXiv preprint arXiv:1908.10084_.
  * Semino et al. (2007) Elena Semino et al. 2007.  Mip: a method for identifying metaphorically used words in discourse.  _Metaphor and symbol_ , 22(1):1–39.
  * Sheng et al. (2019) Emily Sheng, Kai-Wei Chang, Prem Natarajan, and Nanyun Peng. 2019.  The woman worked as a babysitter: On biases in language generation.  In _Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)_ , pages 3398–3403.
  * Shutova et al. (2016) Ekaterina Shutova, Douwe Kiela, and Jean Maillard. 2016.  Black holes and white rabbits: Metaphor identification with visual features.  In _Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies_ , pages 160–170.
  * Shutova et al. (2010) Ekaterina Shutova, Lin Sun, and Anna Korhonen. 2010.  Metaphor identification using verb and noun clustering.  In _Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)_ , pages 1002–1010.
  * Speer et al. (2017) Robyn Speer, Joshua Chin, and Catherine Havasi. 2017.  Conceptnet 5.5: An open multilingual graph of general knowledge.  _In Thirty-First AAAI Conference on Artificial Intelligence._
  * Steen (2010) Gerard Steen. 2010.  _A method for linguistic metaphor identification: From MIP to MIPVU_ , volume 14.  John Benjamins Publishing.
  * Stowe et al. (2019) Kevin Stowe, Sarah Moeller, Laura Michaelis, and Martha Palmer. 2019.  Linguistic analysis improves neural metaphor detection.  In _Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)_ , pages 362–371.
  * Stowe and Palmer (2018) Kevin Stowe and Martha Palmer. 2018.  Leveraging syntactic constructions for metaphor identification.  In _Proceedings of the Workshop on Figurative Language Processing_ , pages 17–26.
  * Stowe et al. (2020) Kevin Stowe, Leonardo Ribeiro, and Iryna Gurevych. 2020.  Metaphoric paraphrase generation.  _arXiv preprint arXiv:2002.12854_.
  * Su et al. (2020) Chuandong Su, Fumiyo Fukumoto, Xiaoxi Huang, Jiyi Li, Rongbo Wang, and Zhiqun Chen. 2020.  Deepmet: A reading comprehension paradigm for token-level metaphor detection.  In _Proceedings of the Second Workshop on Figurative Language Processing_ , pages 30–39.
  * Tekiroğlu et al. (2015) Serra Sinem Tekiroğlu, Gözde Özbal, and Carlo Strapparava. 2015.  Exploring sensorial features for metaphor identification.  In _Proceedings of the Third Workshop on Metaphor in NLP_ , pages 31–39.
  * Terai and Nakagawa (2010) Asuka Terai and Masanori Nakagawa. 2010.  A computational system of metaphor generation with evaluation mechanism.  In _International Conference on Artificial Neural Networks_ , pages 142–147. Springer.
  * Tsvetkov et al. (2014) Yulia Tsvetkov, Leonid Boytsov, Anatole Gershman, Eric Nyberg, and Chris Dyer. 2014\.  Metaphor detection with cross-lingual model transfer.  In _Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)_ , pages 248–258.
  * Veale et al. (2016) Tony Veale, Ekaterina Shutova, and Beata Beigman Klebanov. 2016.  Metaphor: A computational perspective.  _Synthesis Lectures on Human Language Technologies_ , 9(1):1–160.
  * Wallace et al. (2019) Eric Wallace, Shi Feng, Nikhil Kandpal, Matt Gardner, and Sameer Singh. 2019.  Universal adversarial triggers for attacking and analyzing nlp.  _arXiv preprint arXiv:1908.07125_.
  * Wilks (1975) Yorick Wilks. 1975.  A preferential, pattern-seeking, semantics for natural language inference.  _Artificial intelligence_ , 6(1):53–74.
  * Wilks (1978) Yorick Wilks. 1978.  Making preferences more active.  _Artificial intelligence_ , 11(3):197–223.
  * Yu and Wan (2019) Zhiwei Yu and Xiaojun Wan. 2019.  How to avoid sentences spelling boring? towards a neural approach to unsupervised metaphor generation.  In _Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)_ , pages 861–871, Minneapolis, Minnesota. Association for Computational Linguistics.
  * Zhang et al. (2019) Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. 2019\.  Bertscore: Evaluating text generation with bert.  _arXiv preprint arXiv:1904.09675_.

##  Appendix A Appendix

For retrieving commonsense symbolism of the sentences, we use the pre-trained
COMET model 333https://github.com/atcbosselut/comet-commonsense and retrieve
top 5 candidates for each input.

  1. 1.

No of Parameters: For metaphor detection at token level we use BERT-base-cased
model (110M). For generation we use the BART large checkpoint (400M
parameters) and use the implementation by FAIRSEQ Ott et al. (2019)
444https://github.com/pytorch/fairseq/tree/master/examples/bart. For
discriminative decoding we use RoBERTa large model (355M)

  2. 2.

No of Epochs: For metaphor detection at token level for parallel data creation
we fine-tune it for 3 epochs. We fine-tune pre-trained BART for 70 epochs for
MERMAID model and save best model based on validation perplexity. For
discriminator we fine-tune RoBERTa-large model for 10 epoch and save the
checkpoint for best validation accuracy

  3. 3.

Training Time: For metaphor detection training time is 40 minutes.Our training
time is 280 minutes for BART. For discriminator we train it for 60 minutes

  4. 4.

Hardware Configuration: We use 4 RTX 2080 GPU

  5. 5.

Training Hyper parameters: We use the same parameters mentioned in the github
repo where BART was fine-tuned for CNN-DM summarization task with the
exception of MAX-TOKENS (size of each mini-batch, in terms of the number of
tokens.) being 1024 for us. For discrminator finetuning of roberta we use same
parameters as RTE task
555https://github.com/pytorch/fairseq/blob/master/examples/roberta/README.glue.md

  6. 6.

Decoding Strategy & Hyper Parameters:For decoding we generate metaphors from
our models using a top-k random sampling scheme Fan et al. (2018). At each
timestep, the model generates the probability of each word in the vocabulary
being the likely next word. We randomly sample from the k = 5 most likely
candidates from this distribution.