Satire, irony, and sarcasm are techniques that are typically used humorously or critically, rather than deceptively; they can occasionally be mistaken for factual reporting, akin to fake news. These techniques can be applied at a more granular level, allowing satirical information to be incorporated into news articles. In this paper, we introduce the first sentence-level dataset for Romanian satire detection for news articles, called SeLeRoSa. The dataset comprises 13,873 manually annotated sentences spanning various domains, including social issues, IT, science, and movies. With the rise and recent progress of large language models (LLMs) in the natural language processing literature, LLMs have demonstrated enhanced capabilities to tackle various tasks in zero-shot settings. We evaluate multiple baseline models based on LLMs in both zero-shot and fine-tuning settings, as well as transformer-based models. Our findings reveal the current limitations of these models in the sentence-level satire detection task, paving the way for new research directions.

Therapy, interviews, and emergency services assisted by artificial intelligence (AI) are applications where speech emotion recognition (SER) plays an essential role, for which performance and robustness are subject to improvement. Deep learning approaches have proven effective in SER; nevertheless, they can underperform when exposed to adversarial attacks. In this paper, we explore and enhance architectures, such as convolutional neural networks with long short-term memory (CNN-LSTM), AlexNet, VGG16, Convolutional Vision Transformer (CvT), Vision Transformer (ViT), and LeViT, by finding the suitable setup for SER models regarding speech processing, network hyperparameters, spectrogram augmentations, and adversarial examples. We apply our methodology to Romanian and German SER datasets and achieve state-of-the-art results, with 89.81% validation weighted accuracy and 98.09% average weighted accuracy on the trained models. Our highly robust models reach complete adversarial defense and up to 5.56% weighted accuracy improvement when attacked. We also show how adversarial attacks influence model behavior in SER through explainable AI techniques.

Complex Word Identification (CWI) is an essential step in the lexical simplification task and has recently become a task on its own.Some variations of this binary classification task have emerged, such as lexical complexity prediction (LCP) and complexity evaluation of multi-word expressions (MWE).Large language models (LLMs) recently became popular in the Natural Language Processing community because of their versatility and capability to solve unseen tasks in zero/few-shot settings.Our work investigates LLM usage, specifically open-source models such as Llama 2, Llama 3, and Vicuna v1.5, and closed-source, such as ChatGPT-3.5turboand GPT-4o, in the CWI, LCP, and MWE settings.We evaluate zero-shot, few-shot, and fine-tuning settings and show that LLMs struggle in certain conditions or achieve comparable results against existing methods.In addition, we provide some views on meta-learning combined with prompt learning.In the end, we conclude that the current state of LLMs cannot or barely outperform existing methods, which are usually much smaller.

Keyphrase identification and classification is a Natural Language Processing and Information Retrieval task that involves extracting relevant groups of words from a given text related to the main topic. In this work, we focus on extracting keyphrases from scientific documents. We introduce TA-DA, a Topic-Aware Domain Adaptation framework for keyphrase extraction that integrates Multi-Task Learning with Adversarial Training and Domain Adaptation. Our approach improves performance over baseline models by up to 5% in the exact match of the F1-score.

Complex word identification (CWI) is a cornerstone process towards proper text simplification.CWI is highly dependent on context, whereas its difficulty is augmented by the scarcity of available datasets which vary greatly in terms of domains and languages.As such, it becomes increasingly more difficult to develop a robust model that generalizes across a wide array of input examples.In this paper, we propose a novel training technique for the CWI task based on domain adaptation to improve the target character and context representations.This technique addresses the problem of working with multiple domains, inasmuch as it creates a way of smoothing the differences between the explored datasets.Moreover, we also propose a similar auxiliary task, namely text simplification, that can be used to complement lexical complexity prediction.Our model obtains a boost of up to 2.42% in terms of Pearson Correlation Coefficients in contrast to vanilla training techniques, when considering the CompLex from the Lexical Complexity Prediction 2021 dataset.At the same time, we obtain an increase of 3% in Pearson scores, while considering a cross-lingual setup relying on the Complex Word Identification 2018 dataset.In addition, our model yields state-ofthe-art results in terms of Mean Absolute Error.