Author: Ali Sartaz Khan
Supervisor: Ellen Riloff
Affiliation: Department of Computer Science, University of Arizona
We utilize a variety of NLP models, including BERT, LSTM, and CNNs, to identify hyperbolic statements across different datasets. These efforts are crucial for enhancing language understanding models so that they can correctly interpret exaggerations without taking them literally.
Improving model performance by incorporating contextual insights, such as literal paraphrases, to better differentiate between exaggerated and literal statements.
Training models like RoBERTa to simultaneously detect hyperbole and metaphor shows how leveraging multitask learning frameworks can enhance the detection capabilities of NLP systems.
Exploring creative applications of hyperbole generation using techniques like over-generation and ranking contributes to more sophisticated NLP tools capable of producing or transforming hyperbolic content.
The first few studies focus on detecting sarcasm and hyperbole in social media platforms like Twitter. The study “Signaling sarcasm: From hyperbole to hashtag” investigates the use of hashtags to mark sarcasm in Dutch tweets (Liebrecht, Kunneman, and van den Bosch, 2013), while “Features and Categories of Hyperbole in Cyberbullying Discourse on Social Media” analyzes the linguistic features of hyperbole in cyberbullying utterances on social media (Akoury et al., 2022).
These studies highlight the importance of understanding sarcasm and hyperbole in online communication, as they can convey implicit meanings and nuances that might be challenging to detect using traditional NLP techniques.
Several studies address the issue of exaggeration in science communication, particularly in how scientific findings are represented in the media. The “NLP Analysis of Exaggerated Claims in Science News” and “Semi-Supervised Exaggeration Detection of Health Science Press Releases” papers focus on detecting and mitigating the distortion of scientific findings in media reports (Sumner et al., 2014; Al-Hity and Islam, 2021).
Additionally, “A Simple Three-Step Approach for the Automatic Detection of Exaggerated Statements in Health Science News” proposes a three-step method for automatically detecting exaggerated statements in media coverage of scientific research (Braverman, John, and Roberts, 2021).
The study “Probing for Hyperbole in Pre-Trained Language Models” takes a different approach by investigating how hyperbolic information is encoded in pre-trained language models (PLMs). The researchers use edge and minimal description length (MDL) probing experiments to explore the representations of hyperbole within these models (Akoury et al., 2023).
This research area contributes to our understanding of how language models handle figurative language and could potentially lead to improvements in tasks such as sarcasm detection, sentiment analysis, and natural language generation.
This dataset was created specifically for studying hyperbole in the Chinese language. It consists of 4,762 sentences, split into 2,680 hyperbolic and 2,082 non-hyperbolic. The dataset was annotated by having experts create hyperbolic versions of non-hyperbolic sentences and vice versa (Kong et al., 2020).
This dataset includes 709 hyperbolic utterances, 709 literal paraphrases, and 709 non-hyperbolic sentences, totaling 2,127 sentences. The HYPO dataset provides a rich set of examples sourced from various contexts, such as headlines and cartoons, labeled as either hyperbolic or non-hyperbolic (Troiano et al., 2018).
Part of the broader HYPO initiative, this dataset is used for advanced text generation experiments, including hyperbole generation. It is a more recent addition to the HYPO datasets, aiding in the development of models that can generate exaggerated text (Zhang and Wan, 2022).
This dataset focuses on figurative language and includes a list of words categorized into literal and nonliteral clusters. It is used to study the recognition of nonliteral language through unsupervised learning methods (Birke and Sarkar, 2006).
This dataset includes metaphor annotations and is used to study the broader context of figurative language, which encompasses hyperbole as well (Mohler et al., 2016).
The HYPO dataset stands out for its comprehensive design, which facilitates both the detection and understanding of hyperboles in natural language.
The HYPO dataset consists of 709 hyperbolic utterances sourced from diverse mediums like headlines and cartoons. It also includes equal numbers of literal paraphrases and non-hyperbolic sentences using the same words, totaling 2,127 sentences. This design helps contrast hyperbolic statements against their non-hyperbolic counterparts to better train models on distinguishing exaggerated language.
For the task of classifying hyperbolic and non-hyperbolic statements, a BERT-based model was developed and refined using the HYPO dataset (Troiano et al., 2018). The model architecture consisted of a pre-trained BERT model followed by a linear classification layer on top.
The dataset was split into training, development, and test sets using a specific approach. 20% of the data was randomly pulled out as the test set, 10% was selected as the development set, and the remaining 70% constituted the training set. Importantly, all columns (hyperbolic sentence, literal paraphrase, and non-hyperbolic sentence) were kept together in the same split to prevent information leakage.
| Metric | Hyperbole (%) | Non-Hyperbole (%) |
|---|---|---|
| Precision | 82.86 | 76.57 |
| Recall | 41.43 | 95.71 |
| F-1 Score | 55.24 | 85.08 |
These metrics provide a comprehensive view of the model’s performance. The precision metric indicates that the model is more reliable in identifying true hyperbolic statements. However, the recall for hyperbolic statements stands at 41.43%, which suggests challenges in detecting subtler forms of exaggeration.
The model is effective in detecting non-hyperbolic statements with high reliability.
Detecting hyperbolic statements presents challenges, particularly in recognizing less obvious exaggerations, as reflected by the lower recall rate.
Incorporating more diverse examples of hyperbole, including subtler forms, might train the model to recognize a broader range of exaggerations.
Adjusting model parameters or exploring more complex architectures could enhance sensitivity to nuanced language features typical of hyperbole.
Multitasking learning could aid the model by simultaneously learning related tasks, such as sarcasm detection, which shares linguistic characteristics with hyperbole.