The YNU-HPCC team participated in Subtask A of SemEval‑2025 Task 11, "Bridging the Gap in Text‑Based Emotion," using a RoBERTa model with a single prediction head to process one emotion at a time. Their system achieved an official ranking score of 0.44 across all languages after translating the dataset into English with Google Translate. Analysis showed that a single head outperformed six simultaneous heads and that training on the uniformly translated English data improved results.
By Hao Yang, Jin Wang, Xuejie Zhang
arXiv:2609.05806v1 Announce Type: new
Abstract: Emotion Recognition in Conversations (ERC) aims to identify speakers' emotions in multi-turn dialogue. Accurate emotion recognition can support a wide...
By Amir Ben Khalifa, Fanny Bezancon, Amine Trabelsi, Bessam Abdulrazak
arXiv:2606. 27717v1 Announce Type: cross Abstract: Prosodic emphasis varies across languages, emotions, and speaking styles, yet existing emphasis detection models are largely trained and evaluated on monolingual neutral read speech.
By Megan Wei, Deepali Aneja, Jiaqi Su, Yunyun Wang, Haonan Chen, Zeyu Jin
arXiv:2608. 15619v1 Announce Type: new Abstract: Emotion recognition from text keeps improving on benchmarks, yet whether an accuracy ceiling has been reached is seldom asked with discipline.
By Keito Inoshita
arXiv:2609.39453v1 Announce Type: cross
Abstract: Speech emotion recognition (SER) is the task of assigning emotion labels to utterances. Early systems relied on acoustic features, whereas recent app...
By Hezhao Zhang, Thomas Hain
arXiv:2604. 07801v2 Announce Type: replace-cross Abstract: Large language models are trained and evaluated on quantitative reasoning tasks written in clean, emotionally neutral language.
By Atahan Dokme, Benjamin Reichman, Larry Heck
arXiv:2608.29613v1 Announce Type: cross
Abstract: Function vectors (FVs) have recently emerged as a promising mechanism for steering the behavior of large language models (LLMs) by injecting task-spe...
By Jieying Xue, Phuong Minh Nguyen, Minh Le Nguyen, Shogo Okada
The study examines how emotions are represented across layers of large language models (LLMs) by probing eight 1B–9B open‑weight models on three datasets (Twitter, Reddit, autobiographical narratives). It finds that the optimal probing layer varies systematically with the dataset, moving from near‑input layers to deeper layers, and that targeted forward‑pass interventions on these layers degrade performance more than random interventions. Additionally, the selected layers transfer across datasets and emotion categories, and early‑exit representations from these layers outperform full‑depth exits by an average of 6.9 percentage points.
By Tian Fang, Ga\"el Guibon, Davide Buscaldi
arXiv:2605. 13801v2 Announce Type: replace-cross Abstract: As generative AI models such as large language models (LLMs) become more pervasive, ensuring the safety, robustness, and overall trustworthiness of these systems is paramount.
By Deepak Pandita, Flip Korn, Chris Welty, Christopher M. Homan
arXiv:2601.06631v2 Announce Type: replace
Abstract: Building NLP systems for subjective tasks requires one to ensure their alignment to contrasting human values. We propose the MultiCalibrated Subjec...
By Mohammed Fayiz Parappan, Ricardo Henao
arXiv:2309.15670v3 Announce Type: replace
Abstract: In recent years, Sentiment Analysis (SA) and Emotion Recognition (ER) have been increasingly popular in the Bangla language, which is the seventh m...
By Sumit Kumar Banshal, Sajal Das, Shumaiya Akter Shammi, Narayan Ranjan Chakraborty, Vedika Gupta, Mousumi Karmakar
arXiv:2502. 08266v3 Announce Type: replace-cross Abstract: Hate speech detection is a crucial task, especially on social media where harmful content can spread quickly.
By Somaiyeh Dehghan, Mehmet Umut Sen, Berrin Yanikoglu