We present EPIC (English Perspectivist Irony Corpus), the first annotated corpus for irony analysis based on the principles of data perspectivism. The corpus contains short conversations from social media in five regional varieties of English, and it is annotated by contributors from five countries corresponding to those varieties. We analyse the resource along the perspectives induced by the diversity of the annotators, in terms of origin, age, and gender, and the relationship between these dimensions, irony, and the topics of conversation. We validate EPIC by creating perspective-aware models that encode the perspectives of annotators grouped according to their demographic characteristics. Firstly, the performance of perspectivist models confirms that different annotators induce very different models. Secondly, in the classification of ironic and non-ironic texts, perspectivist models prove to be generally more confident than the non-perspectivist ones. Furthermore, comparing the performance on a perspective-based test set with those achieved on a gold standard test set, we can observe how perspectivist models tend to detect more precisely the positive class, showing their ability to capture the different perceptions of irony. Thanks to these models, we are moreover able to show interesting insights about the variation in the perception of irony by the different groups of annotators, such as among different generations and nationalities.

Datasets used to train deep learning models in industrial settings often exhibit skewed distributions with some samples repeated a large number of times.This paper presents a simple yet effective solution to reduce the increased burden of repeated computation on redundant datasets.Our approach eliminates duplicates at the batch level, without altering the data distribution observed by the model, making it model-agnostic and easy to implement as a plug-and-play module. We also provide a mathematical expression to estimate the reduction in training time that our approach provides. Through empirical evidence, we show that our approach significantly reduces training times on various models across datasets with varying redundancy factors, without impacting their performance on the Named Entity Recognition task, both on publicly available datasets and in real industrial settings.In the latter, the approach speeds training by up to 87%, and by 46% on average, with a drop in model performance of 0.2% relative at worst.We finally release a modular and reusable codebase to further advance research in this area.

In real-world systems, an important requirement for model updates is to avoid regressions in user experience caused by flips of previously correct classifications to incorrect ones. Multiple techniques for that have been proposed in the recent literature. In this paper, we apply one such technique, focal distillation, to model updates in a goal-oriented dialog system and assess its usefulness in practice. In particular, we evaluate its effectiveness for key language understanding tasks, including sentence classification and sequence labeling tasks, we further assess its effect when applied to repeated model updates over time, and test its compatibility with mislabeled data. Our experiments on a public benchmark and data from a deployed dialog system demonstrate that focal distillation can substantially reduce regressions, at only minor drops in accuracy, and that it further outperforms naive supervised training in challenging mislabeled data and label expansion settings.