Source-linked AI summary
A Transformer-based approach to Irony and Sarcasm detection
Rolandos Alexandros Potamias, Georgios Siolas, Andreas - Georgios Stafylopatis
TL;DR
The paper addresses the difficulty of identifying figurative language in short social-media texts, where literal and intended meanings can diverge. It combines pre-trained RoBERTa with a recurrent convolutional neural network and reports state-of-the-art performance across four benchmark datasets.
Problem
Figurative language remains difficult to identify in short social-media texts because contradictory and non-literal meanings complicate interpretation and sentiment analysis.
Method
The paper proposes RCNN-RoBERTa, an end-to-end model combining pre-trained RoBERTa with a recurrent convolutional neural network and minimal preprocessing.
Results
The proposed approach outperforms prior approaches on binary classification tasks across the evaluated benchmark datasets, with the RCNN reinforcing RoBERTa by 2-5% F1 score.
Takeaways & Limitations
The results support using pre-trained Transformer representations enhanced with recurrent convolutional processing for figurative-language detection across benchmark tasks.
Takeaways & Limitations
Prior studies often rely on costly engineered features, large dictionaries, or datasets that do not support comparison, motivating the paper’s constrained design and benchmark evaluation.
Abstract
from arXiv · showhide
Figurative Language (FL) seems ubiquitous in all social-media discussion forums and chats, posing extra challenges to sentiment analysis endeavors. Identification of FL schemas in short texts remains largely an unresolved issue in the broader field of Natural Language Processing (NLP), mainly due to their contradictory and metaphorical meaning content. The main FL expression forms are sarcasm, irony and metaphor. In the present paper we employ advanced Deep Learning (DL) methodologies to tackle the problem of identifying the aforementioned FL forms. Significantly extending our previous work [71], we propose a neural network methodology that builds on a recently proposed pre-trained transformer-based network architecture which, is further enhanced with the employment and devise of a recurrent convolutional neural network (RCNN). With this set-up, data preprocessing is kept in minimum. The performance of the devised hybrid neural architecture is tested on four benchmark datasets, and contrasted with other relevant state of the art methodologies and systems. Results demonstrate that the proposed methodology achieves state of the art performance under all benchmark datasets, outperforming, even by a large margin, all other methodologies and published studies.
1 Introduction
Figurative language complicates sentiment analysis because literal wording can diverge from intended meaning, while prior approaches often rely on costly preprocessing or engineered features. The paper proposes an end-to-end transformer-based approach with minimal preprocessing and benchmark evaluation.
- Motivation: Social-media figurative language can make sentiment polarity differ from literal wording, especially for ironic and sarcastic expressions.Missing facial expressions and voice tone further motivate context-aware approaches.
- Figurative language: Figurative language denotes a contradiction between an utterance’s literal and non-literal meanings and includes irony, sarcasm, and metaphor.The paper treats ironic and sarcastic expressions as forms of indirect denial.
- Research gap: Existing NLP systems often lose performance on figurative language, motivating methods that detect, separate, and classify its forms.The paper identifies figurative-language processing as difficult for computational approaches.
- Research gap: Prior studies use time-consuming preprocessing, engineered features, or large dictionaries, and some rely on datasets that cannot support fair comparison.The cited drawbacks include stemming, tagging, emoji processing, feature engineering, and API-collected datasets.
- Contribution: The proposed end-to-end methodology avoids hand-crafted features and lexicons, uses only de-capitalization preprocessing, and evaluates on benchmark datasets.The paper describes this as an unsupervised pre-trained Transformer method for capturing multiple figurative-language forms.
2 Literature Review
Prior figurative-language research spans contradiction-based, content/context-based, and deep-learning approaches, alongside shared tasks focused on irony, sarcasm, metaphor, and sentiment.
- Scope: The literature review groups irony and sarcasm studies together despite acknowledging that the two phenomena are not identical.It also notes research across languages including Italian, Japanese, Spanish, and Greek.
- Irony and sarcasm detection: Earlier social-media studies modeled irony and sarcasm using unexpectedness and contradiction factors.Subsequent work measured unexpectedness with corpus frequency, tweet morphology, and tree-based classifiers.
- Content and context: Content- and context-based systems used n-grams, acronyms, adverbs, word frequencies, semantic features, LIWC, syntactic features, and psycholinguistic features.These approaches commonly represented textual content through manually selected attributes.
- Deep learning: Deep-learning studies used recurrent networks, word embeddings, CNN-LSTM units, and attentive RNNs to model dependencies in figurative-language text.Word embeddings were described as important components of these neural architectures.
- Shared evaluation: SemEval-2015 evaluated sentiment in ironic, sarcastic, and metaphorical tweets using lexicons, syntactic features, regression, decision trees, SVMs, and semi-supervised methods.The task included multiple team submissions with different feature and classifier combinations.
3 The background: Recent advances in Natural Language Processing
Recent NLP advances emphasize transfer learning, contextual representations, and Transformer architectures trained on large unlabeled corpora before task-specific supervision.
- Transfer learning: Unsupervised and transfer-learning approaches address limited annotated datasets and costly data collection by reusing knowledge in new conditions.The paper identifies pre-trained embeddings and transfer architectures as increasingly relevant to domain adaptation.
- Embeddings: Traditional pre-trained embeddings such as GloVe and Word2Vec capture semantic representations, but word meaning can vary with context.The paper contrasts these representations with contextual approaches.
- Contextual representations: Universal Sentence Encoder maps complete sentences into latent vectors and offers transformer and alternative variants with computation–accuracy trade-offs.The passage describes sentence-level contextual representation rather than word-only embeddings.
- Transformer architecture: Transformers address long-range dependency limitations in encoder–decoder methods by relying entirely on attention-based processing.The background presents Transformers as a distinct architecture for sequence modeling.
- BERT: BERT is pre-trained with masked-token and next-sentence tasks, then lightly adapted using a supervised task-specific classifier.Its pre-training uses Wikipedia, BookCorpus, and WordPiece embeddings, with the final [CLS] state aggregated for classification.
- RoBERTa: RoBERTa is presented as an optimized BERT-style model trained with substantially more data than the original architecture.The supplied passage specifies 160GB compared with an omitted comparison quantity.
4 Proposed Method: Recurrent CNN RoBERTA (RCNN-RoBERTa)
RCNN-RoBERTa combines pre-trained RoBERTa representations with recurrent and convolutional processing to capture contextual and informative relationships for figurative-language detection.
- Architecture: RCNN-RoBERTa feeds RoBERTa’s rich word embeddings into recurrent convolutional processing to capture contextual information and dependencies.The design is motivated by enhancing pre-trained network outputs with another neural network.
- Training setup: The selected hyperparameters were chosen through grid search with 5-fold cross-validation, selecting the settings with the best performance.The procedure is documented in Table 1.
- Architecture: The recurrent component captures temporal relationships, while convolution and max-pooling reduce recurrent bias toward later words and identify semantic relationships.The model addresses the limitations of recurrent ordering bias and CNN dependence on kernel sizes.
- Architecture: A bidirectional LSTM layer is incorporated into the modified RCNN to capture unbiased recurrent informative relationships within text.The passage describes this as part of the architecture’s contextual-information modeling.
5 Experimental Results
The experiments compare RCNN-RoBERTa with advanced neural and published baselines across four figurative-language datasets using standard classification and sentiment metrics. RCNN-RoBERTa outperforms competing approaches on binary tasks, with reported gains in F1, AUC, and MSE.
- Evaluation setup: The study evaluates binary tasks with Accuracy, Precision, Recall, F1-score, and AUC, while Task 11 additionally uses cosine similarity and MSE.Train/test splits follow dataset authors or published-study settings.
- Results: RCNN-RoBERTa outperforms all compared approaches and published results on the respective binary classification tasks.This finding covers the SemEval-2018, Reddit Politics, and Sarcastic Riloff comparisons.
- Results: 2–5% F1 score gains and higher AUC indicate that the RCNN architecture reinforces RoBERTa and increases classification confidence.The model also behaves better than RoBERTa on the imbalanced Riloff and SemEval-2015 datasets.
- Results: RCNN-RoBERTa improves MSE by almost 33.5% over DESC, reporting 2,480 vs. 1,450, while DESC achieves higher cosine similarity at 0,820 vs. 0,810.These results concern the SemEval-2015 Task 11 sentiment-scoring task.
6 Conclusion
The conclusion presents RCNN-RoBERTa as a transformer-based approach for figurative-language detection that minimizes preprocessing and engineered feature extraction. It reports state-of-the-art performance across four benchmark datasets and six metrics.
- Conclusion: RCNN-RoBERTa combines pre-trained RoBERTa with a recurrent convolutional neural network for figurative-language detection.The method targets social-media figurative language while minimizing preprocessing and engineered feature extraction.
- Conclusion: The model achieves state-of-the-art performance across four benchmark datasets and six metrics.It outperforms BERT, XLnet, ELMo, and USE under all reported metrics, sometimes by a large factor.
A Appendix
The appendix reports implementation settings for comparator models, including BERT and RoBERTa training configurations and classifier layers.
- Comparator settings: The ELMo comparator mean-pools contextualized representations into a 1024-dimensional vector before two dense ReLU layers.The dense layers contain 256 and 64 units.
- Model settings: BERT models use 12 layers, 768 hidden units, and 12 attention heads, trained for 3 epochs at learning rate 2 × 10^-5.The models use 10^-5 weight decay and include cased and uncased BERT-base variants.
- Model settings: RoBERTa follows the BERT training setting and uses two dense fully connected layers as its output classifier.BERT, RoBERTa, and XLnet implementations use the pytorch-transformers library.