P romotion G o at L e W i D i-2025: Enhancing Multilingual Irony Detection with Data-Augmented Ensembles and L 1 Loss (2025)

Abstract: This paper presents our system for the Learning with Disagreements (LeWiDi-2025) shared task (Leonardelli et al., 2025), which targets the challenges of interpretative variation in multilingual irony detection. We introduce a unified framework that models annotator disagreement through soft-label prediction, multilingual adaptation and robustness-oriented training. Our approach integrates tailored data augmentation strategies (i.e., lexical swaps, prompt-based reformulation and back-translation) with an ensemble learning scheme to enhance sensitivity to contextual and cultural nuances. To better align predictions with human-annotated probability distributions, we compare multiple loss functions, including cross-entropy, Kullback—Leibler divergence and L1 loss, the latter showing the strongest compatibility with the Average Manhattan Distance evaluation metric. Comprehensive ablation studies reveal that data augmentation and ensemble learning consistently improve performance across languages, with their combination delivering the largest gains. The results demonstrate the effectiveness of combining augmentation diversity, metric-compatible optimisation and ensemble aggregation for tackling interpretative variation in multilingual irony detection.

## introduction irony is a complex linguistic phenomenon in which the intended meaning of an utterance diverges from, or even contradicts, its literal expression. it often relies on contextual incongruity, implicit stance, or shared background knowledge, making it highly dependent on both linguistic and pragmatic cues. this complexity renders irony detection a particularly challenging task for computational systems, especially when extended to multilingual and multicultural contexts where the expression and interpretation of irony may vary substantially. in such settings, human annotators frequently disagree on whether a given utterance is ironic. this disagreement stems not only from the inherent ambiguity of language but also from differences in cultural norms, humour styles, and pragmatic expectations. the multipico (multilingual perspectivist irony corpus, mp) @xcite , used in the lewidi-2025 shared task @xcite , explicitly captures this variability by providing soft labels (i.e., empirical distributions over annotator judgments) rather than single hard labels. modelling these distributions requires systems capable of representing annotation uncertainty and preserving distributional information in both training and inference phases. we address this challenge by adopting a perspectivist framing of irony detection that emphasises multilingual generalisation and probabilistic supervision. our system is built upon a multilingual transformer, such as xlm-r @xcite , and incorporates several task-specific strategies: (1) a document representation pipeline that encodes post-reply pairs to preserve conversational context; our contributions are as follows: the source code for this paper is publicly available on github 1 . ## related work irony and sarcasm detection. detecting irony and sarcasm in text has been a long-standing challenge in computational linguistics due to its reliance on implicit meaning, context, and cultural cues. early approaches relied on handcrafted features such as sentiment contrast or punctuation patterns @xcite . with the rise of deep learning, more robust methods using recurrent networks and attention mechanisms were introduced @xcite . recent work has explored context-aware transformers, modelling not just the utterance but also conversational history or speaker intent @xcite . while effective in monolingual settings, extending irony detection to multilingual and multicultural contexts remains an open problem, especially under limited annotated data. ## system overview we consider the irony detection problem as a binary classification task. given a set of dialogues d composed of post-reply pairs (x ## dataset and experimental setup we use the multipico (mp) corpus @xcite , a multilingual dataset comprising short post-reply exchanges collected from twitter and reddit. each reply is annotated in the context of the preceding post by approximately five crowd workers. annotators label whether the reply is ironic, resulting in a binary classification task. instead of collapsing annotations into hard labels, the corpus provides soft labels (i.e., distributions over the two classes) to preserve inter-annotator disagreement and enable models to learn from nuanced and perspectivist supervision. the mp corpus spans nine languages: arabic, dutch, english, french, german, hindi, italian, portuguese and spanish. this makes it particularly well-suited for evaluating systems in multilingual and cross-cultural settings. the distribution of training, development, and test instances for each language is shown in we use the hyperparameters provided in the transformers library for the xlm-roberta-base model. training is performed using the adam optimiser with a linear learning rate schedule and early stopping based on validation loss. all preprocessing, tokenisation and batching are handled using the hugging face framework. ## results our system achieved competitive performance in the lewidi-2025 shared task, highlighting the effectiveness of its multilingual architecture and softlabel modelling approach. in the following subsections, we present detailed evaluations, including augmentation and loss function ablations, ensemble comparison, as well as cross-lingual analyses. all the following experiments are using the same training arguments, the only differences between them are the data augmentation methods, loss function, and ensemble method. due to the absence of publicly released gold labels for the test set, most validation results were obtained via the codabench evaluation platform. language-wise analyses (section 5.4) could not be conducted on the hidden test set; for these, we report results on the development set instead. ## conclusions we presented a unified system for the lewidi-2025 shared task that addresses the challenges of annotator disagreement in multilingual irony detection. our approach integrates complementary data augmentation strategies, loss functions tailored to the evaluation metric, and an ensemble framework to improve alignment with human-annotated soft labels. experiments demonstrate that the combination of augmentation and ensemble learning yields substantial reductions in average manhattan distance over strong baselines, with l1 loss proving particularly effective for soft-label prediction under this metric. these findings underscore the value of jointly leveraging data diversity, metric-compatible optimisation, and model aggregation to better capture interpretative variation in multilingual and culturally nuanced nlp tasks. future work will explore more context-aware augmentation methods and adaptive ensemble schemes to further enhance cross-lingual robustness.