Venue: NAACL 2022

# MOVER: Mask, Over-generate and Rank for Hyperbolic Generation

 Yunxiang Zhang, Xiaojun Wan

Wangxuan Institute of Computer Technology, Peking University

The MOE Key Laboratory of Computational Linguistics, Peking University

{yx.zhang,wanxiaojun}@pku.edu.cn

###### Abstract

Despite being a common figure of speech, hyperbole is under-researched in Figurative Language Processing. In this paper, we tackle the challenging task of hyperbole generation to transfer a literal sentence into its hyperbolic paraphrase. To address the lack of available hyperbolic sentences, we construct HYPO-XL, the first large-scale English hyperbole corpus containing 17,862 hyperbolic sentences in a non-trivial way. Based on our corpus, we propose an unsupervised method for hyperbole generation that does not require parallel literal-hyperbolic pairs. During training, we fine-tune BART Lewis et al. (2020) to infill masked hyperbolic spans of sentences from HYPO-XL. During inference, we mask part of an input literal sentence and over-generate multiple possible hyperbolic versions. Then a BERT-based ranker selects the best candidate by hyperbolicity and paraphrase quality. Automatic and human evaluation results show that our model is effective at generating hyperbolic paraphrase sentences and outperforms several baseline systems.

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Footnote †: Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 6018 - 6030

July 10-15, 2022 ©2022 Association for Computational Linguistics

## 1 Introduction

Hyperbole is a figure of speech that deliberately exaggerates a claim or statement to show emphasis or express emotions. If a referent has a feature X, a hyperbole exceeds the credible limits of fact in the given context and presents it as having more of that X than warranted by reality Claridge (2010). Take the following example, "_I won't wait for you: it took you centuries to get dressed._" It over-blows the time for someone to get dressed with a single word "_centuries_" and thus creates a heightened effect. From a syntactic point of view, Claridge (2010) classifies hyperbole into word-level, phrase-level and clause-level types, and conclude that the former two types are more common in English. Although hyperbole is considered as the second most frequent figurative device Kreuz and Roberts (1993), it has received less empirical attention in the NLP community. Recently Tian et al. (2021) addressed the generation of _clause-level_ hyperbole. In this paper, we instead focus on _word-level_ and _phrase-level_ hyperbole, which can be unified as span-level hyperbole.

To tackle the hyperbole generation problem we need to address three main challenges:

* The lack of training data that either consists of large-scale hyperbolic sentences or literal-hyperbole pairs, which are necessary to train an unsupervised or supervised model.
* The tendency of generative language models to produce literal text rather than hyperbololes.
* Trade-off between content preservation and hyperbolic effect of the generated sentences.

In order to address the above challenges, we propose **MOVER** (Mask, **OVEr**-generate and **R**ank), an unsupervised approach to generating hyperbolic paraphrase from literal input. Our approach does not require parallel data for training, thus alleviating the issue of scarce data. Still, we need a non-parallel corpus containing as much hyperbolic sentences as possible. To this end, we first build a large-scale English hyperbole corpus HYPO-XL in a weakly supervised way.

Based on the intuition that the hyperbolic effect of a sentence is realized by a single word or phrase within it, we introduce a sub-task of hyperbolic span extraction. We identify several possible n-grams of a hyperbolic sentence that can cause the hyperbolic bent with syntactic and semantic features. We apply this masking approach to sentences in HYPO-XL and teach a pretrained seq2seq transformer, BART Lewis et al. (2020), to infill the words in missing hyperbolic spans. This increases the probability of generating hyperbolic texts instead of literal ones. During inference, given a single literal sentence, our system provides multiple masked versions for inputs to BART and gen

erates potential hyperbolic sentences accordingly. To select the best one for output, we leverage a BERT-based ranker to achieve a satisfying trade-off between hyperbolicity and paraphrase quality.

Our contributions are three-fold:

* We construct the first large-scale hyperbole corpus HYPO-XL in a non-trivial way. The corpus is publicly available,1 contributing to the Figurative Language Processing (FLP) community by facilitating the development of computational study of hyperbole. Footnote 1: Code and data are available at [https://github.com/yunx-z/MOVER](https://github.com/yunx-z/MOVER).
* We propose an unsupervised approach for hyperbole generation that falls into the "overgenerate-and-rank" paradigm Heilman and Smith (2009).
* We benchmark our system against several baselines and we compare their performances by pair-wise manual evaluations to demonstrate the effectiveness of our approach.

## 2 HYPO-XL: Hyperbole Corpus Collection

The availability of large-scale corpora can facilitate the development of figurative language generation with pretrained models, as is shown by Chakrabarty et al. (2020) on simile generation and Chakrabarty et al. (2021) on metaphor generation. However, datasets for hyperbole are scarce. Troiano et al. (2018) built an English corpus HYPO containing 709 triplets \([hypo,para,non\_hypo]\), where \(hypo\) refers to a hyperbolic sentence, \(para\) denotes the literal paraphrase of \(hypo\) and \(non\_hypo\) means a non-hyperbolic sentence that contains the same hyperbolic word or phrase as \(hypo\) but with a literal connotation. The size of this dataset is too small to train a deep learning model for hyperbole detection and generation. To tackle the lack of hyperbole data, we propose to enlarge the hyperbolic sentences of HYPO in a weakly supervised way and build a large-scale English corpus of 17,862 hyperbolic sentences, namely HYPO-XL. We would like to point out that this is a _non-parallel_ corpus containing only hyperbolic sentences without their paraphrase counterparts, because our hyperbole generation approach (Section 3) does not require parallel training data.

The creation of HYPO-XL consists of two steps:

1. We first train a BERT-based binary classifier on HYPO and retrieve possible hyperbolic sentences from an online corpus.
2. We manually label a subset of the retrieved sentences, denoted HYPO-L, and retrain our hyperbole detection model to identify hyperbolic sentences from the same retrieval corpus with higher confidence.

### Automatic Hyperbolic Detection

Hyperbole detection is a supervised binary classification problem where we predict whether a sentence is hyperbolic or not Kong et al. (2020). We fine-tune a BERT-base model Devlin et al. (2019) on the hyperbole detection dataset HYPO Troiano et al. (2018). In experiment, we randomly split the data into 567 (80%) hyperbolic sentences, with their literal counterparts (\(para\) and \(non\_hypo\)) as negative samples, in training set and 71 (10%) in development set and 71 (10%) in test set. Our model achieves an accuracy of 80% on the test set, which is much better than the highest reported accuracy (72%) of traditional algorithms in Troiano et al. (2018).

Once we obtain this BERT-based hyperbole detection model, the next step is to retrieve hyperbolic sentences from a corpus. Following Chakrabarty et al. (2020), we use Sentencedict.com,2 an online sentence dictionary as the retrieval corpus. We remove duplicate and incomplete sentences (without initial capital) in the corpus, resulting in a collection of 767,531 sentences. Then we identify 93,297 (12.2 %) sentences predicted positive by our model as pseudo-hyperbolic.

Footnote 2: [https://sentencedict.com/](https://sentencedict.com/)

### HYPO-L: Human Annotation of Pseudo-labeled Data

Due to the small size of training set, pseudo-labeled data tend to have lower confidence score (i.e., the prediction probability). To improve the precision of our model,3 we further fine-tune it with our human-annotated data, namely HYPO-L. We randomly sample 5,000 examples from the 93,297 positive predictions and invite students with proficiency in English to label them as hyperbolic or not. For each sentence, two annotators provide their judgements. We only keep items with unanimous judgments (i.e. both of the two annotators mark the sentence as hyperbolic or non-hyperbolic) to ensure the reliability 

of annotated data. In this way, 3,226 (64.5%) out of 5,000 annotations are left in HYPO-L. This percentage of unanimous judgments (i.e., raw agreement, RA) is comparable to 58.5% in the creation of HYPO Troiano et al. (2018). To be specific, HYPO-L consists of 1,007 (31.2%) hyperbolic sentences (positive samples) and 2,219 (68.8%) literal ones (negative samples).

We continue to train the previous HYPO-fine-tuned BERT on HYPO-L and the test accuracy is 80%,4 which we consider as an acceptable metric for hyperbole detection. Finally we apply the BERT-based detection model to the retrieval corpus again and retain sentences whose prediction probabilities for positive class exceed a certain threshold.5 This results in HYPO-XL, a large-scale corpus of 17,862 (2.3%) hyperbolic sentences. We provide a brief comparison of HYPO, HYPO-L and HYPO-XL in Table 1 to further clarify the data collection process.

Footnote 4: We separate 10% data of HYPO-L for development and another 10% for testing.

Footnote 5: Based on manual inspection of predicted results, we set the threshold as 0.8 to trade-off between precision and recall.

### Corpus Analysis

Since HYPO-XL is built in a weakly supervised way with only a few human labeled data samples, we conduct a quality analysis to investigate how many sentences in the corpus are _actually_ hyperbolic. We randomly sample 100 instances from HYPO-XL and manually label them as hyperbole or non-hyperbole. Only six sentences are not hyperbole. This precision of 94% is on par with 92% on another figurative language corpus of simile Zhang et al. (2021). Actually we can tolerate a bit noise in the corpus since the primary goal of HYPO-XL is to facilitate hyperbole _generation_ instead of _detection_, and a small proportion of non-hyperbolic sentences as input will not harm our proposed method.6 Table 2 shows the statistics of hyperbolic text spans (defined in Section 3.1) for the rest 94 real hyperbololes. We also provide additional analyses in Appendix A.

Footnote 6: We further explain the reason in Section 3.2

## 3 Hyperbole Generation

We propose an unsupervised approach to generate hyperbolic paraphrase from a literal sentence with BART Lewis et al. (2020) such that we do not require parallel literal-hyperbole pairs.7 An overview of our hyperbole generation pipeline is shown in Figure 1. It consists of two steps during training:

Footnote 7: We note that training our model still relies on instances from a specialized hyperbole corpus.

1. **Mask.** Given a hyperbolic sentence from HYPO-XL, we identify multiple text spans that can possibly produce the hyperbolic meaning of a sentence, based on two features (POS n-gram and unexpectedness score). For each identified text span, we replace it with the <mask> token to remove hyperbolic attribute of the input. \(N\) text spans will result in \(N\) masked inputs, respectively.
2. **Infill.** We fine-tune BART to fill the masked spans of input sentences. The model learns to generate hyperbolic words or phrases that are pertinent to the context. During inference, there are three steps:

1. **Mask.** Given a literal sentence, we apply POSngram-only masking to produce multiple input sentences.
2. **Over-generate.** BART generates one sentence from a masked input, resulting in multiple candidates.
3. **Rank.** Candidates are ranked by their hyperbolicity and relevance to the source literal sentence. The one with highest score is selected as the final output.

\begin{table}
\begin{tabular}{l c c c c} \hline \hline Dataset & \# Hypo. & \# Non. & \# Para. & \# Total \\ \hline HYPO & 709 & 698 & 709 & 2,116 \\ HYPO-L & 1,007 & 2,219 & - & 3,226 \\ HYPO-XL & 17,862 & - & - & 17,862 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparision of different hyperbole datasets (corpora) in terms of hyperbolic (Hypo.), non-hyperbolic (Non.) and paraphrase (Para.) sentences.

\begin{table}
\begin{tabular}{p{113.8pt} p{113.8pt}} \hline \hline Measurement & Value \\ \hline \% Non-hypo & 6\% \\ \hline \# Avg hypo span tokens & 2.23 \\ \% Long hypo spans (\(>\) 1 token) & 37\% \\ \# Distinct hypo spans & 85 \\ \# Distinct POS-ngrams of hypo spans & 39 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistics of 100 random samples from HYPOC XL, of which 6 are actually non-hyperboles “Non-hypo”). The statistics of hyperbolic text spans (“hypo span”) are calculated for the rest 94 real hyperbololes.

 

We dub our hyperbole generation system **MOVER**(Mask, **OVEr**-generate and **R**ank). We apply masking technique to map both the hyperbolic (training input) and literal (test input) sentences into a same "space" where the masked sentence can be transformed into hyperbole by BART. It falls into the "overgenerate-and-rank" paradigm Heilman and Smith (2009) since many candidates are available after the generation step. The remainder of this section details the three main modules: hyperbolic span masking (Section 3.1), BART-based span infilling (Section 3.2) and the hyperbole ranker (Section 3.3).

### Mask: Hyperbolic Span Masking

We make a simple observation that the hyperbolic effect of a sentence is commonly localized to a single word or a phrase, which is also supported by a corpus-based linguistic study on hyperbole Claridge (2010). For example, the word _marathon_ in _"My evening jog with Bill turned into a **marathon"_** overstates the jogging distance and causes the sentence to be hyperbolic. This inspires us to leverage the "delete-and-generate" strategy Li et al. (2018) for hyperbole generation. Concretely, a literal sentence can be transformed into its hyperbolic counterpart via hyperbolic span extraction and replacement. We propose to extract hyperbolic spans based on POS n-gram (syntactic) and unexpected-sens (semantic) features.

POS N-gramWe extract POS n-gram patterns of hyperbole from the training set of HYPO dataset8 and obtain 262 distinct POS n-grams. As a motivating example, the following three hyperbolic spans, _"faster than light", "sweeter than honey", "whiter than snow"_, share the same POS n-gram of "JJR+IN+NN".

Footnote 8: The hyperbolic spans are not explicitly provided in the HYPO dataset, so we take the maximum word overlap between \(hypo\) and \(non\_hyppo\) (Section 2) as the hyperbolic spans.

UnexpectednessHyperbolic spans are less coherent with the literal contexts and thus their vector representations are distant from the context vectors. Troiano et al. (2018) have verified this intuition with the unexpectedness metric. They define the unexpectedness score \(U_{s}\) of a sentence \(s\) with the token sequence \(\{x_{0},x_{1},...,x_{N}\}\) as the average cosine distance among all of its word pairs.

\[U_{s}=\underset{i,j\in[0,N],i\neq j}{\text{average}}(\text{cosine\_distance}(v _{i},v_{j})) \tag{1}\]

where \(v_{i}\) denotes the word embedding vector of token \(x_{i}\). Similarly, we define the unexpectedness score \(U_{n}\) of an n-gram \(\{x_{k},x_{k+1},...,x_{k+n-1}\}\) in a sentence \(s\) as the average cosine distance among word pairs that consist of one word inside the n-gram and the other outside.

\[U_{n}=\underset{i\in[k,k+n-1]}{\text{average}}(\text{cosine\_distance}( v_{i},v_{j})) \tag{2}\]

Figure 1: Overview of our approach to unsupervised hyperbole generation. Literal sentences are in yellow boxes, masked sentences are in blue boxes and hyperbolic sentences are in green boxes.

 

Text spans with higher unexpectedness scores tend to be hyperbolic. Figure 2 illustrates the cosine distance of the word pairs in the sentence "_I've drowned myself trying to help you_". The words in the span "_drowned myself_" are distant from other words in terms of word embedding similarity.

For the masking step during training, we extract all text spans in the original input hyperbolic sentences that match one of the hyperbolic POS n-grams. Then we rank them by their unexpectedness scores and choose top-3 items as the masked spans.9 For the masking step during inference, we simply mask all the spans that match hyperbolic POS n-grams, since the span unexpectedness score is not applicable to a literal input. We evaluate the accuracy of our hyperbolic span masking approach on the development set of the HYPO dataset. The proportion of exact match (EM) Rajpurkar et al. (2016) between our top-3 masked spans with the human-labeled spans is 86%, which shows that our simple method based on the above-mentioned hand-crafted features is effective for the task of hyperbolic span extraction.

Footnote 9: This means that at least 2/3 of the identified spans should not be hyperbolic, but this will not harm the training of our hyperbole generation model, which is explained in Section 3.2

### Over-generate: Hyperbolic Text Infilling with BART

In order to generate hyperbolic and coherent text from the masked span, we leverage the text span infilling ability of BART Lewis et al. (2020), a pre-trained sequence2sequence model with a denoising autoencoder and an autoregressive autodecoder. During its pretraining, it learns to reconstruct the corrupted noised text. One of the noising transformations is random span masking, which teaches BART to predict the multiple tokens missing from a span. During our training process, we fine-tune BART by treating the masked hyperbolic sentence as the encoder source and the original one as the decoder target. This can change the probability distribution when decoding tokens and increase the chance of generating a hyperbolic, rather than literal, text span conditioned on the context. During inference, BART fills the masked span of a literal sentence with possible hyperbolic words.

Note that if the masked span of an input sentence is actually not hyperbolic, then fine-tuning on this example will just enhance the reconstruction ability of BART, which will not exert negative effects on hyperbole generation. This can give rise to our tolerance for non-hyperbolic sentences in the training corpus (Section 2.3) and non-hyperbolic masked span (Section 3.1).

### Rank: Hyperbole Ranker

Recall that for each literal input during inference, we apply POS-ngram-based masking, produce different masked versions of the sentence, and generate multiple output candidates. Obviously, not all masking spans are suitable for infilling hyperbolic words due to the noise of masking. To select the best candidate for final output, we introduce a hyperbole ranker which sorts candidate sentences by their degree of hyperbolicity and relevance to the source inputs. For hyperbolicity evaluation, we leverage the BERT-based hyperbole detection model fine-tuned on HYPO and HYPO-L (Section 2.2) to assign a hyperbole score (i.e., prediction probability) for each candidate. For the evaluation of content preservation, we train a pairwise model to predict whether hyperbolic sentence A is a paraphrase of a literal sentence B. To this end, we use the distilled RoBERTa-base model checkpoint10 pretrained on large scale paraphrase data provided by Sentence-Transformer Reimers and Gurevych (2019). It calculates the cosine similarity between the literal input and the candidate as the paraphrase score. We fine-tune the checkpoint on the training set of the HYPO dataset, where we treat the pairs of \(hypo\) and \(para\) as positive examples, and

Figure 2: A visualization of the cosine distance matrix of the hyperbolic sentence “_I’ve drowned myself trying to help you_”.

 

pairs of \(hypo\) and \(non\_hypo\) as negative examples (Section 2). The accuracy on test set is 93%.

Now that we obtain the hyperbole score hypo\((c)\) and the paraphrase score para\((c)\) for candidate \(c\), we propose an intuitive scoring function score\((\cdot)\) as below:

\[{\rm score}(s)=\begin{cases}\text{hypo}(s)&\text{para}(s)\in(\gamma,1-\epsilon)\\ 0&\text{else}\end{cases} \tag{3}\]

Here we filter out a candidate if its paraphrase score is lower than a specific threshold \(\gamma\) or it is almost the same as the original input (i.e., the paraphrase score is extremely close to 1). For diversity purposes, we do not allow our system to simply copy the literal input as its output. We then rank the remaining candidates according to their hyperbole score and select the best one as the final output.11

Footnote 11: If all candidates are filtered out by their paraphrase scores (i.e. they all have the zero final scores), we will select the one with the highest hyperbolic score among all candidates.

## 4 Experiments

There are no existing models applied to the task of word- or phrase-level hyperbole generation. To compare the quality of the generated hyperboles, we benchmark our MOVER system against three baseline systems adapted from related tasks.

### Baseline Systems

Retrieve **(R1)**Following Nikolov et al. (2020), we implement a simple information retrieval baseline, which retrieves the closest hyperbolic sentence as the output (i.e., the highest cosine similarity) from HYPO-XL, using the hyperbole paraphrase detection model para\((\cdot)\) in Section 3.3. The outputs of this baseline system should be hyperbolic yet have limited relevance to the input.

Retrieve, Replace and Rank **(R3)**We first retrieve the top-5 most similar sentences from HYPO-XL like the R1 baseline. Then we apply hyperbolic span extraction in Section 3.1 to find 3 text spans for each retrieved sentence. We replace the text spans in a literal input sentence with retrieved hyperbolic spans if two spans share the same POS n-gram. Since this replacement method may result in multiple modified sentences, we select the best one with the hyperbole ranker in Section 3.3. If there are no matched text spans, we fall back to R1 baseline and return the most similar retrieved sentence verbatim. In fact, this baseline substitutes the BART generation model in MOVER system with a simpler retrieval approach, which can demonstrate the hyperbole generation ability of BART.

BartInspired by Chakrabarty et al. (2020), we replace the text infilling model in Section 3.2 with a non-intuned off-the-shelf BART,12 because BART has already been pretrained to predict tokens from a masked span.

Footnote 12: We also tried to fine-tune BART on the 567 literal-hyperbole pairs from the training set of HYPO dataset in an end-to-end supervised fashion, but the model just copy the input for all instances (same as COPY in Table 3) and is unable to generate meaningful output due to small amount of training data. Besides, we test the performance of a BART-based paraphrase generation model, which is BART finetuned on QQP Wang et al. (2018) and PAWS Zhang et al. (2019) datasets. We still find that 50% of the outputs from the paraphrase model just copy the input. Therefore, we do not consider these two BART-based systems hereafter.

### Implementation Details

We use 16,075 (90%) samples in HYPO-XL for training our MOVER system and the rest 1,787 sentences for validation. For POS Tagging in Section 3.1, we use Stanford CoreNLP Manning et al. (2014). For the word embedding, we use 840B 300-dimension version of GloVe vectors Pennington et al. (2014). For BART in Section 3.2, we use the BART-base checkpoint instead of BART-large due to limited computing resources and leverage the implementation by Huggingface Wolf et al. (2020). We fine-tune pretrained BART for 16 epochs. For the parameters of the hyperbole ranker in Section 3.3, we set \(\gamma=0.8\) and \(\epsilon=0.001\) by manual inspection of the ranking results on the development set of the HYPO dataset.

### Evaluation Criteria

Automatic EvaluationBLEU Papineni et al. (2002) reflects the lexical overlap between the generated and the ground-truth text. BERTScore Zhang et al. (2020) computes the similarity using contextual embeddings. These are common metrics for text generation. We use the 71 literal sentences (\(para\)) in the test set of HYPO dataset as test inputs and their corresponding hyperbolic sentences (\(hypo\)) as gold references. We report the BLEU and BERTScore metrics for generated sentences compared against human written hypoles.

Human EvaluationAutomated metrics are not reliable on their own to evaluate methods to generate figurative language Novikova et al. (2017) so we also conduct pair-wise comparisons manually 

(Shao et al., 2019). We evaluate the generation results from the 71 testing literal sentences. Each pair of texts (ours vs. a baseline / human reference) is given preference (win, lose or tie) by five people with proficiency in English. We use a set of four criteria adapted from Chakrabarty et al. (2021) to evaluate the generated outputs: 1) **Fluency (Flu.)**: Which sentence is more fluent and grammatical? 2) **Hyperbolicity (Hypo.)**: Which sentence is more hyperbolic? 3) **Creativity (Crea.)**: Which sentence is more creative? 4) **Relevance (Rel.)**: Which sentence is more relevant to the input literal sentence?

### Results

Automatic EvaluationTable 3 shows the automatic evaluation results of our system compared to different baselines. MOVER outperforms all three baselines on these two metrics. However, BLEU and BERTScore are far not comprehensive evaluation measures for our hyperbole generation task, since there are only a few modifications from literal to hyperbole and thus there is a lot of overlap between the generated sentence and the source sentence. Even a naive system (COPY in Table 3) that simply returns the literal input verbatim as output Krishna et al. (2020) can achieve the highest performance. As a result, automatic metrics are not suitable for evaluating models that tend to copy input as output.

Human EvaluationThe inter-annotator agreement of raw human evaluation results in terms of Fleiss' kappa Fleiss (1971) is 0.212, which indicates fair agreement Landis and Koch (1977). We take a conservative approach and only consider items with an absolute majority label, i.e., at least three of the five labelers choose the same preference (win/lose/tie). On average there are 61 (86%) items left for each baseline-criteria pair that satisfy this requirement. On this subset of items, Fleiss' Kappa increases to 0.278 (fair agreement). This degree of agreement is acceptable compared to other sentence revision tasks (e.g., 0.322 by Tan and Lee (2014) and 0.263 by Afrin and Litman (2018)) since it is hard to discern the subtle changing effect caused by local revision.

The annotation results in Table 4 are the absolute majority vote (majority \(>=\) 3) from the 5 annotators for each item. The results show that our model mostly outperforms (Win% \(>\) Lose%) other baselines in the four metrics, except for creativity on R1. Because R1 directly retrieves human written hypoles from HYPO-XL and is not strict about the relevance, it has the advantage of being more creative naturally. An example of this is shown in Table 5. Our model achieves a balance between generating hyperbolic output while preserving content, indicating the effectiveness of the "overgenerate-and-rank" mechanism. It is also worth noting that in terms of hyperbolicity, MOVER even performs better than human for 16.7% of the test cases. Table 5 shows a case where MOVER is rated higher than human.

\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline MOVER vs. & Flu. & Hypo. & Crea. & Rel. \\ \cline{2-9}  & W\% & L\% & W\% & L\% & W\% & L\% & W\% & L\% \\ \hline R1 & **79.7** & 1.7 & **52.4** & 47.6 & 33.9 & **66.1** & **94.2** & 4.3 \\ R3 & **35.8** & 11.3 & **52.5** & 36.1 & **50.0** & 38.5 & **52.6** & 29.8 \\ BART & **26.2** & 19.7 & **67.7** & 11.3 & **61.0** & 10.2 & **49.2** & 31.7 \\ HUMAN & **22.0** & 18.6 & 16.7 & **81.8** & 14.3 & **84.3** & **46.8** & 37.1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Pairwise human comparison between MOVER and other baseline systems. Win[W]% (Lose[L]%) is the percentage of MOVER considered better (worse) than a baseline system. The rest are ties.

\begin{table}
\begin{tabular}{l l r} \hline \hline System & BLEU & BERTScore \\ \hline R1 & 2.02 & 0.229 \\ R3 & 33.25 & 0.520 \\ BART & 33.57 & 0.596 \\ \hline MOVER & **39.43** & **0.624** \\ w/o para score & 39.22 & 0.604 \\ w/o hypo ranker & 34.83 & 0.610 \\ \hline COPY & 51.69 & 0.711 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Automatic evaluation results on the test set of HYPO dataset.

\begin{table}
\begin{tabular}{l l r r r r r} \hline \hline System & Sentence & F. & H. C. & R. \\ \hline LTTERAL & Being out of fashion is very & - & - & - & - \\  & bad. & & - & - & - \\ MOVER & Being out of fashion is _sheer_ & - & - & - & - \\ R1 & _Their music will never go_ out & T & **W** & L & **W** \\  & of fashion. & & & & \\ R3 & Being out of fashion is _richly_ & T & **W** & **W** & T \\  & bad. & & & & \\ BART & Being out of fashion is very & T & **W** & **W** & T \\  & _difficult_. & & & & \\ HUMAN & _Better be out of the world than_ & **W** & **W** & **L** & **W** \\  & out of the fashion. & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Pairwise evaluation results (Win[W], Lose[L], Tiel[T]) in terms of **F**luency, **H**yperbolicity, **C**reativity and **R**elevance between MOVER and the generated outputs of the baseline systems. Changed text spans are in _italic_. More examples are given in Appendix B.

 

Case StudyTable 5 shows a group of generated examples from different systems. MOVER changes the phrase "very bad" in the original input to an extreme expression "sheer hell", which captures the sentiment polarity of the original sentence while providing a hyperbolic effect. R1 retrieves a hyperbolic but irrelevant sentence. R3 replaces the word "very" with "richly", which is not coherent to the context, although the word "richly" may introduce some hyperbolic effects. BART just generates a literal sentence, which seems to be a simple paraphrase. Although human reference provides a valid hyperbolic paraphrase, the annotators prefer our version in terms of fluency, hyperbolicity and relevance. Since our system makes fewer edits to the input than to the human reference, we are more likely to win in fluency and relevance. Also, the generated hyperbolic span "sheer hell" presents a more extreme exaggeration than "out of the world" according to the human annotators.

Table 6 shows the over-generation results for a literal input, with their hyperbole and paraphrase scores. On the one hand, our system can generate different hyperbolic versions, like the generated words "cannot", "unyielding", and "alive". This is reasonable since there might be multiple hyperbolic paraphrases for a single sentence. It is only for comparison with other baselines that we have to use the ranker to keep only one output, which inevitably undermines the strength of our approach. On the other hand, our ranker filters out the sentence if the infilled text violates the original meaning, which can be seen in the last row of Table 6. In this way, we gain explicit control over hyperbolicity and relevance through a scoring function, and endow MOVER with more explainability.

Despite the interesting results, we also observe the following types of errors in the generated outputs:

* The output is a paraphrase instead of hyperbolic: "_My aim is very certain_" \(\rightarrow\) "_My aim is very clear_".
* The degree of exaggeration is not enough: "_The news has been exaggerated_" \(\rightarrow\) "_The news has been greatly exaggerated_".
* The output is not meaningful: "_I'd love to hang out every day_" \(\rightarrow\) "_I'd love to live every day_". We believe that incorporating more commonsense knowledge and generating freeform hyperbolics beyond word- or phrase-level substitutions are promising for future improvement.

Ablation StudyWe investigate the impact of removing partial or all information during the ranking stage. The results are shown in Table 3. Specifically, if we rank multiple generated outputs by only borehole score (w/o para score), or randomly select one as the output (w/o hypo ranker), the performance will become worse. Note that we do not report the ablation result for ranking only by paraphrase score (w/o hypo score), because it has the same problem with COPY: a generated sentence that directly copies the input will result in the highest paraphrase score and thus be selected as the final output.

Furthermore, we note that the experiments on R3 and BART also serve as ablation studies for the text infilling model in Section 3.2 as they substitute the fine-tuned BART with a retrieve-and-replace method and a non-fine-tuned BART, respectively.

## 5 Related Work

Hyperbole CorpusTroiano et al. (2018) built the HYPO dataset consisting of 709 hyperbolic sentences with human-written paraphrases and lexically overlapping non-hyperbolic counterparts. Kong et al. (2020) also built a Chinese hyperbole

\begin{table}
\begin{tabular}{l c c c} \hline \hline Generated Hyperbole \(s\) & hypo\((s)\) & para\((s)\) & score\((s)\) \\ \hline You have ravished me away by a power I _cannot_ resist. & 0.962 & 0.954 & 0.962 \\ You have ravished me away by a power I find _unyielding_ to resist. & 0.960 & 0.959 & 0.960 \\ You have ravished me _alive_ by a power I find difficult to resist. & 0.954 & 0.931 & 0.954 \\ You have _driven_ me away by a power I find difficult to resist. & 0.858 & 0.914 & 0.858 \\ You have ravished me away _with a beauty_ I find difficult to resist. & 0.958 & 0.778 & 0.000 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Intermediate results of the input literal sentence “_You have ravished me away by a power I find difficult to resist_” after the over-generation steps (Section 3.2). Their ranking scores (Section 3.3) are displayed in the second to the fourth columns. Generated hyperbolic text spans are in _italic_.

 

dataset with 2680 hyperboles. Our HYPO-L and HYPO-XL are substantially larger than HYPO and we hope they can facilitate computational research on hyperbole detection and generation.

Figure Language GenerationAs a figure of speech, hyperbole generation is related to the general task of figurative language generation. Previous studies have tackled the generation of metaphor Yu and Wan (2019); Stowe et al. (2020); Chakrabarty et al. (2021); Stowe et al. (2021), silie Chakrabarty et al. (2020); Zhang et al. (2021), idiom Zhou et al. (2021), pun Yu et al. (2018); Luo et al. (2019); He et al. (2019); Yu et al. (2020), and sarcasm Chakrabarty et al. (2020). HypoGen Tian et al. (2021) is a concurrent work with ours on hyperbolic generation. However, we share a different point of view and the two methods are not directly comparable. They tackle the generation of _clause-level_ hyperboles and frame it as a sentence _completion_ task, while we focus on the _word-level_ or _phrase-level_ ones and frame it as a sentence _editing_ task. In addition, their collected hyperboles and generated outputs are limited to the "\(so...that\)" pattern while we do not posit constraints on sentence patterns.

Unsupervised Text Style TransferRecent advances on unsupervised text style transfer Hu et al. (2017); Subramanian et al. (2018); Luo et al. (2019); Zeng et al. (2020) focus on transferring from one text attribute to another without parallel data. Jin et al. (2020) classify existing methods into three main branches: _disentanglement_, _prototype editing_, and _pseudo-parallel corpus construction_. We argue that hyperbole generation is different from text style transfer. First, it is unclear whether "literal" and "hyperbolic" can be treated as "styles", especially the former one. Because "literal" sentences do not have any specific characteristics at all, there are no attribute markers Li et al. (2018) in the input sentences, and thus many text style transfer methods based on _prototype editing_ cannot work. Second, the hyperbolic span can be lexically separable from, yet strongly dependent on, the context Section3.1. On the contrary, _disentanglement-based_ approaches for text style transfer aim to separate content and style via latent representation learning. Third, MOVER could also be used for _constructing pseudo-parallel corpus_ of literal-hyperbole pairs given enough literal sentences as input, which is beyond the scope of this work.

Unsupervised Paraphrase GenerationUnsupervised paraphrase generation models Wieting et al. (2017); Zhang et al. (2019); Roy and Grangier (2019); Huang and Chang (2021) do not require paraphrase pairs for training. Although hyperbole generation also needs content preservation and lacks parallel training data, it is still different from paraphrase generation because we need to create a balance between paraphrasing and exaggerating. We further note that the task of metaphor generation Chakrabarty et al. (2021), which replaces a verb (e.g., "_The scream filled the night_" \(\rightarrow\) "_The scream pierced the night_"), is also independent of paraphrase generation.

## 6 Conclusion

We tackle the challenging task of figurative language generation: hyperbole generation from literal sentences. We build the first large-scale hyperbole corpus HYPO-XL and propose an unsupervised approach MOVER for generating hyperbole in a controllable way. The results of automatic and human evaluation show that our model is successful in generating hyperbole. The proposed generation pipeline has better interpretability and flexibility compared to the potential end-to-end methods. In future, we plan to apply our "mask-overgenerate-rank" approach to the generation of other figurative languages, such as metaphor and irony.

## 7 Ethical Consideration

The HYPO-XL dataset is collected from a public website Sentencedict.com, and we have asked the website owners permission to use their data for research purposes. There is no explicit detail that leaks a user's personal information including name, health, racial or ethnic origin, religious affiliation or beliefs, sexual orientation, etc.

Our proposed method MOVER utilizes the pre-trained language model, which may inherit the bias in the massive training data. It is possible that MOVER is used for malicious purposes, since it does not explicitly filter input sentences with toxicity, bias or offensiveness. Therefore, the output generated by MOVER could potentially be harmful to certain groups or individuals. It is important that interested parties carefully address those biases before applying the model to real-world situations 



## Acknowledgements

This work was supported by National Science Foundation of China (No. 62161160339), National Key R&D Program of China (No.2018YFB1005100), State Key Laboratory of Media Convergence Production Technology and Systems and Key Laboratory of Science, Techonology and Standard in Press Industry (Key Laboratory of Intelligent Press Media Technology). We appreciate the anonymous reviewers for their helpful comments. Xiaojun Wan is the corresponding auhor.

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## Appendix A Additional Dataset Statistics

We annotate the hyperbolic spans (Section 3.1) for the 94 real hyperboles in Section 2.3 and show some examples of the most common POS n-grams of hyperbolic spans in Table 7. We further follow Troiano et al. (2018) to annotate the types of exaggeration along three dimensions: "measurable", "possible" and "conventional". A hyperpole is "measurable" if it exaggerates something which is objective and quantifiable. A hyperpole is rated as "possible" if it denotes an extreme but conceivable situation. A hyperpole is judged as "conventional" if it does not express an idea in a creative way. However, we note that there are no absolute answers for these three questions and the annotation results may be subjective. Each hyperpole is either YES or NO for each dimension and the reported numbers in Table 8 are for YES.

## Appendix B More Generated Examples

Table 9 shows more examples of output generated from different systems and human references





\begin{table}
\begin{tabular}{c c l} \hline \hline POS & \# & Hyperbolic Example \\ \hline NN & 19 & His words confirmed _everything_. \\ RB & 15 & He descanted _endlessly_ upon the wonders of his trip. \\ JJ & 14 & Youth means _limitless_ possibilities. \\ \hline \hline \end{tabular}
\end{table}
Table 7: Three most common POS n-grams of hyperbolic spans in 94 randomly sampled hyperboles from HYPO-XL. The hyperbolic spans are in _italic_.

\begin{table}
\begin{tabular}{l p{280.0pt} r r r r} \hline \hline System & Sentence & Flu. Hypo. & Crea. & Rel. \\ \hline LITERAL & At that point, the presidency was hard to recover. & - & - & - & - \\ MOVER & At that point the presidency was _virtually impossible_ to recover. & - & - & - & - \\ R1 & _The destruction of a President with its collapse of executive authority was too staggering to contemplate_. & W & W & T & **W** \\ R3 & At that point the presidency was _staggering_ to recover. & W & W & L & **W** \\ BART & At that point the presidency was _too fragile_ to recover & T & W & T & T \\ HUMAN & At that point, the presidency was _fatally wounded_. & T & W & W & **W** \\ \hline LITERAL & His piano playing is very bad. & - & - & - & - \\ MOVER & His piano playing is _beyond_ bad. & - & - & - & - \\ R1 & Her piano playing is _absolute magic_. & T & T & L & **W** \\ R3 & His piano _guitar_ is very bad. & T & T & L & L \\ BART & His piano playing is very _good_. & T & W & W & **W** \\ HOMAN & His piano playing is _enough to make Beethoven turn in his grave_. & T & L & L & **W** \\ \hline LITERAL & The professor humiliated me in front of the class. & - & - & - & - \\ MOVER & The professor humiliated me in _every conceivable way_. & - & - & - & - \\ R1 & _She infected the whole class with her enthusiasm._ & W & W & W & W \\ R3 & _That lecture_ humiliated me in front of the class. & T & W & T & T \\ BART & The professor humiliated me _and the rest_ of the class. & W & W & W & W \\ HUMAN & The professor _destroyed_ me in front of the class. & T & L & W & W \\ \hline LITERAL & It annoys me when you only drink half of the soda. & - & - & - & - \\ MOVER & It _kills_ me when you only drink half of the soda. & - & - & - & - \\ R1 & _That was the best ice-cream soda I ever tasted._ & T & W & W & W \\ R3 & It annoys me when you only drink _boredom_ of the soda. & T & W & W & T \\ BART & It annoys me when you only drink half of it. & W & W & W & W \\ HUMAN & It _drives me crazy_ when you only drink half of the soda. & T & W & T & T \\ \hline \hline \end{tabular}
\end{table}
Table 8: Three types of exaggeration in 94 randomly sampled hyperboles from HYPO-XL. The hyperbolic spans are in _italic_.

\begin{table}
\begin{tabular}{r l l l l l} \hline \hline Type & \# & Hyperbole Example & & & & \\ Measurable & 44 & At any moment, I feared, the boys could _snap my body in half_ & & & \\  & & with just one concerted shove. & & & & \\ Possible & 27 & The words caused a shiver to _run a fine sharp line_ through her. & & & \\ Conventional & 65 & She is _forever_ picking at the child. & & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Three most common POS n-grams of hyperbolic spans in 94 randomly sampled hyperbololes from HYPO-XL. The hyperbolic spans are in _italic_.