Sentimental analysis (SA) of movie reviews has become an essential means of supporting audiences, filmmakers, and investors to facilitate data-driven marketing, support audience engagement, and maximize audience development returns. Nevertheless, current SA models are yet to overcome potentially daunting problems such as noisy data, ambiguity of language, limited handling of negations, and a loss of rich semantic information during mirror preprocessing that ultimately decreases classification performance. To overcome this hurdle, a unique two-stage strategy entitled Hierarchical Attention-Guided Sentiment Network (HASN) is proposed that enables reliable and interpretable sentiment predictions. The first stage includes a clearly defined preprocessing pipeline of the IMDb dataset that features text cleaning, lemmatizing, removing stop words, negation, TF-IDF filtering of low-frequency terms, and lastly, Word2Vec embeddings, which create semantically consistent and contextually rich representations of potential inputs. The second stage integrates a tri-stream attention recurrent extractor (TARE), which reconstructs and merges previously learned outputs with the learned syntactic and semantic contexts with the benefits of stacked Bi-LSTMs, combines learned syntactic and semantic contexts with multi-head attention to identify the tokens that drive sentiment, and ends with a residual-normalized GRU (ResNorm-GRU), which sequentially refines the sequential process of intake. The attentive conditional random field (Attn-CRF) serves as a classifier by boosting coherence in label predictions through attention weighting with modeling of the sequential dependencies. The experiments with the IMDb dataset demonstrated that the best results detected were the following: 98.09, 98.47, 97.70, and 98.08%. In summary, the HASN framework is a precise, scalable, and interpretable model for analyzing sentiments in movie reviews.

Automatic named entity recognition (NER) is essential for many natural language processing applications, particularly in low-resource languages and those with corpora restricted to a single domain, where the lack of diverse data may hinder cross-domain generalisation. In this context, we attempt to contribute to the Galician NER field by extending manually annotated resources and exploring several approaches to improve the performance of automatic classifiers. To this end, we enrich two new datasets with NER annotations in Galician, which we release in this paper. Through experimental evaluation, we investigate the benefits of fine-tuning transformers on NER data by comparing various monolingual and multilingual BERT models and developing a new extra-small DeBERTa model for Galician. Furthermore, we assess the quality of the NER model by expanding the initial training corpus with additional data from related varieties, including Portuguese and Spanish. For evaluation, we use five Galician corpora, including our two newly annotated datasets and three adapted from existing resources, ensuring assessment across multiple domains. Our findings reveal (1) a positive impact of enhanced cross-lingual training data on improved model performance across five Galician test datasets; (2) similar performance of monolingual and multilingual models depending on the training data; and (3) the competitive effectiveness of extra-small BERT models for Galician NER tasks compared to larger models.

This paper presents a comparative study of neural machine translation (MT) models and large language models, emphasizing translation literalness, lexical diversity, and translation quality. We evaluate open-source neural machine translation (NMT) models, LLaMa 3.1 Instruct, and GPT-4o across nine language pairs in pretrained, fine-tuned, and few-shot prompting settings. This comprehensive analysis underscores the differential strengths of each model, contributing valuable insights into their respective utility in MT. Our findings in the literary and news domains show that fine-tuning reduces translation literalness, allowing freer translations across models, most strongly for LLaMa, which produces the least literal translations across domains. The effect of fine-tuning on lexical diversity is model-dependent: it decreases consistently for LLaMa but remains stable or is improved for NMT. Few-shot prompting, on the other hand, enhances GPT-4o’s lexical diversity across domains and significantly boosts its translation quality in the literary domain, yielding the highest overall COMET scores. Furthermore, our correlation analysis reveals a distinct domain effect: literary translation evaluation rewards diversity, whereas news translation evaluation favors literalness. Overall, the results highlight complementary strengths: few-shot GPT-4o for high-quality, lexically rich translations with low error rates, and fine-tuned LLaMa for freer, less literal translations.

Rapid advancements in education assessment and natural language processing have fundamentally transformed the methodologies employed in multi-dimensional writing assessment. Neural network approaches have shown remarkable effectiveness, obviating the need for handcrafted feature engineering. However, the potential of pre-trained language models for constructing multi-dimensional automated essay scoring (AES) models still needs to be explored. The present study addresses critical challenges, including the resilience of AES models to subtle input perturbations leading to prediction inaccuracies and the elucidation of the intricate mechanisms within deep learning frameworks. We propose an integrated approach leveraging multi-task learning and the pre-trained model Bidirectional Encoder Representations from Transformers (BERT), augmented with adversarial training via the Fast Gradient Sign Method (FGSM), aiming to enhance scoring accuracy and stability in multi-dimensional AES tasks. Findings reveal that BERT-Integrated-MTL-FGSM significantly outperforms existing baselines. Integrating Explainable Artificial Intelligence (XAI) elucidates the complex evaluation processes within the deep learning black box, offering potentially diagnostic feedback for students and assisting educators in delivering individualized instruction guided by word-level attributions across diverse analytic rating scales. These insights enhance the robustness, generalizability, and interpretability of multi-dimensional AES models, thus paving an interdisciplinary pathway for multi-dimensional writing assessment and instruction.

The European Union’s Artificial Intelligence Act changes the governance environment in which natural language processing (NLP) systems are developed and deployed. Its central lesson for Responsible NLP is that responsibility does not follow automatically from a model’s architecture or benchmark score. It is distributed across three layers: a general-purpose model, the task-specific system built around it, and the institution in which that system acts. This piece analyses those layers through recruitment, education, public-service chatbots, public-interest text generation, and general-purpose language models. It shows how the Act turns principles into duties concerning risk management, data governance, documentation, transparency, human oversight, and robustness, together with complaint, explanation, and procedural-accountability mechanisms, while also examining its interaction with data-protection law. The analysis reveals a deeper research challenge: compliance requires an evidence chain connecting intended purpose, affected people, evaluation, institutional workflow, and post-deployment monitoring. Regulation cannot create that evidence by itself. Multilingual fairness, construct validity, meaningful explanation, uncertainty communication, and effective contestability remain scientific and sociotechnical problems. The EU AI Act should therefore be understood as a legal floor for Responsible NLP, not its scientific ceiling.

There have been a number of disturbing posts on social media about large conferences. The good news is that we are past denial. There is agreement that reviewing is broken. Many posts mention toxicity. As a founder of EMNLP, it hurts to admit that there is so much toxicity that I can no longer support the process. I will continue to review for journals and smaller venues, but not for larger conferences until better ways are found to manage the crowds. Enough negativity. What can we do to be part of the solution? Many constructive suggestions have been proposed elsewhere and will be discussed here. Scale is the primary requirement. Traditional processes do not scale with exponential growth. Many attempts to fix the process (like ARR) make a bad situation worse by introducing additional bloat, while failing to address the primary requirement: scale. Process simplification will help. Steps should be removed unless they address the scaling challenge: (a) too many submissions and (b) too few qualified reviewers. A nomination process is proposed to address both (a) and (b).