In this study, we present the results of experiments on text simplification for the Lithuanian language, where we aim to simplify administrative-style texts to the Plain Language level. We selected mT5, mBART, and LT-Llama-2 as the foundational model...
Text simplification reduces the complexity of text while preserving essential information, thus making it more accessible to a broad range of readers, including individuals with cognitive disorders, non-native speakers, children, and the general publ...
Misinformation poses a significant challenge in the digital age, requiring robust methods to detect fake news. This study investigates the effectiveness of using Back Translation (BT) augmentation, specifically transformer-based models, to improve fa...
The emergence of large language models (LLMs) has revolutionized the trajectory of NLP research. Transformers, combined with attention mechanisms, have increased computational power, and massive datasets have led to the emergence of pre-trained large...
This paper presents the development of a neural model pipeline for automatic speech recognition (ASR) and text summarization in Turkmen, a low-resource language with agglutinative morphology. For the ASR task, the MMS-1b-all model (Meta) was employed...
Cross-lingual transfer learning using multilingual models has shown promise for improving performance on natural language processing tasks with limited training data. However, translation can introduce superficial patterns that negatively impact mode...
Speech-to-text translation (S2TT) for low-resource languages remains challenging due to the scarcity of parallel speech translation data and the susceptibility of modular pipelines to error propagation. This paper presents a controlled empirical comp...
This paper presents a quantum-inspired cross-attention alignment approach for abstractive summarization. The motivation is that current neural summarizers often lose key content and produce summaries that are weakly grounded in the source, especially...
The rapid growth of digital content in Urdu has created an urgent need for effective automatic text summarization (ATS) systems. While extractive methods have been widely studied, abstractive summarization for Urdu remains largely unexplored due to t...
Transfer learning offers a practical way to improve neural machine translation in low-resource settings, but its effectiveness depends on both the choice of transfer language and the amount of target-language data available for adaptation. In this st...
Multitarget sentiment analysis extracts the subjective polarity of text from multiple targets simultaneously in a given context. This approach is useful in finance, where opinions about different entities affect the target differently. Examples of po...
Text summarization aims to produce a short summary containing relevant parts from a given text. Due to the lack of data for abstractive summarization on low-resource languages such as Italian, we propose two new original datasets collected from two I...
Kazakh Sign Language (KSL) remains a low-resource language with limited parallel data for automatic translation. This study examines written Kazakh-to-KSL gloss translation with supervised models and preference-based optimization. A Transformer basel...
Recent years have seen a surge of interest in dialogue translation, which is a significant application task for machine translation (MT) technology. However, this has so far not been extensively explored due to its inherent characteristics including...
Automatic summarization is essential for processing the vast quantity of news articles. However, existing methods struggle with factual consistency, hallucinations, and English-centric evaluations. This paper investigates whether Large Language Model...
Telemedicine in emergency contexts presents unique challenges, particularly in multilingual and low-resource settings where accurate, clinical understanding and triage decision support are critical. This paper introduces TriagE-NLU, a novel multiling...
A conversational system is an artificial intelligence application designed to interact with users in natural language, providing accurate and contextually relevant responses. Building such systems for low-resource languages like Swahili presents sign...
This article introduces a freely available Spanish–Mixtec parallel corpus designed to foster natural language processing (NLP) development for an indigenous language that remains digitally low-resourced. The dataset, comprising 14,587 sentence...
Most of the models proposed in the literature for abstractive summarization are generally suitable for the English language but not for other languages. Multilingual models were introduced to address that language constraint, but despite their applic...
Epstein-Barr virus (EBV) is associated with several tumors and generates BamHI A rightward transcript (BART) microRNAs (miRNAs) from BART transcript introns. These BART miRNAs are expressed at higher levels in EBV-associated epithelial malignancies t...
Epstein–Barr virus (EBV) is the first oncogenic DNA virus known to encode microRNAs (miRNAs) and has been implicated in the pathogenesis of multiple malignancies, including a distinct subset of gastric cancers (EBV-associated gastric cancer, EB...
The estimation and mapping of forest stand characteristics are vital because this information is necessary for sustainable forest management. The present study considers the use of a Bayesian additive regression trees (BART) algorithm as a non-parame...
The COVID-19 pandemic caused by the SARS-CoV-2 virus, which first emerged in December 2019, represents an ongoing global public health emergency. Here, we developed an improved and highly sensitive approach to SARS-CoV-2 detection via coupling biolum...
A novel total ensemble (TE) algorithm was developed and compared with random forest optimization (RFO), gradient boosted machines (GBM), partial least squares (PLS), Cubist and Bayesian additive regression tree (BART) algorithms to predict numerous s...
Multimodal knowledge extraction has become increasingly important for applications that require compact and informative representations of text-image data, yet existing models often remain computationally expensive for practical use. To address this...
Toxic cyanobacterial blooms have become a severe global hazard to human and environmental health. Most studies have focused on the relationships between cyanobacterial composition and cyanotoxins production. Yet, little is known about the environment...
Barrett’s esophagus (BE) is a premalignant adaptation to chronic gastroesophageal reflux disease (GERD) and the principal precursor lesion of esophageal adenocarcinoma (EAC). Carbon monoxide (CO), an endogenous gaseous mediator generated by hem...
Regional assessments of atmospheric ammonia must distinguish changes in annual abundance from changes in seasonality and hotspot organization. We analyzed monthly Infrared Atmospheric Sounding Interferometer (IASI) total columns over Liaoning, Jilin...
Gastroesophageal reflux disease (GERD) is associated with inflammatory and neoplastic changes in the esophageal epithelium. Despite widespread PPI use, esophageal adenocarcinoma (EAC) incidence continues to rise, implicating non-acidic reflux compone...
Significant progress in legal natural language processing (NLP) has enabled advancements in tasks such as legal judgment prediction, case retrieval, and question answering. However, the development of analogous technologies for Arabic legal texts rem...
Quality estimation (QE) has recently gained increasing interest as it can predict the quality of machine translation results without a reference translation. QE is an annual shared task at the Conference on Machine Translation (WMT), and most recent...
The molecular processes that predispose the development of Barrett’s esophagus (BE) towards esophageal adenocarcinoma (EAC) induced by gastrointestinal reflux disease (GERD) are still under investigation. In this study, based on a scientific li...
This paper presents a technique to reduce the number of parameters in a transformer-based encoder–decoder architecture by incorporating autoencoders. To discover the optimal compression, we trained different autoencoders on the embedding space...
Epstein–Barr virus (EBV) is a human γ-herpesvirus implicated in several human malignancies, including a wide range of lymphomas. Several molecules encoded by EBV in its latent state are believed to be related to EBV-induced lymphomagenesis, among whi...
This paper describes the use of citizen science-derived data for the creation of a land-use regression (LUR) model for particulate matter (PM2.5 and PMcoarse) for a vulnerable community in Imperial County, California (CA), near the United States (US)...
Estimating pasture biomass has emerged as a promising avenue to assist farmers in identifying the best cutting times for maximizing biomass yield using satellite data. This study aims to develop an innovative framework integrating field and satellite...
The rising prevalence of paediatric type 2 diabetes (T2D) is concerning, particularly with limited medical intervention despite evidence of accelerated disease progression. This study of a Barts Health NHS Trust cohort from 2008 to 2022 aims to eluci...
Epithelial barrier dysfunction is a hallmark of gastroesophageal reflux disease (GERD) related to symptom origination, inflammatory remodeling and carcinogenesis. Alginate-based antireflux medications were previously shown to topically protect agains...
Despite medical advances, gastric-cancer (GC) mortality remains high in Europe. Bacterial infection with Helicobacter pylori (H. pylori) and viral infection with the Epstein–Barr virus (EBV) are associated with the development of both distal and prox...