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Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[4/5]:
- HausaNLP at SemEval-2025 Task 11: Hausa Text Emotion Detection
Sani Abdullahi Sani, Salim Abubakar, Falalu Ibrahim Lawan, Abdulhamid Abubakar, Maryam Bala

Last day to enjoy some #tapas / #pintxos in #HeartOfSpain in #Tampere for the

ReXGroundingCT: A 3D Chest CT Dataset for Segmentation of Findings from Free-Text Reports
Mohammed Baharoon, Luyang Luo, Michael Moritz, Abhinav Kumar, Sung Eun Kim, Xiaoman Zhang, Miao Zhu, Mahmoud Hussain Alabbad, Maha Sbayel Alhazmi, Neel P. Mistry, Kent Ryan Kleinschmidt, Brady Chrisler, Sathvik Suryadevara, Sri Sai Dinesh Jaliparthi, Noah Michael Prudlo, Mark David Marino, Jeremy Palacio, Rithvik Akula, Hong-Yu Zhou, Ibrahim Ethem Hamamci, Scott J. Adams, Hassan Rayhan AlOmaish, P…

ReXGroundingCT: A 3D Chest CT Dataset for Segmentation of Findings from Free-Text Reports
We present ReXGroundingCT, the first publicly available dataset to link free-text radiology findings with pixel-level segmentations in 3D chest CT scans that is manually annotated. While prior datasets have relied on structured labels or predefined categories, ReXGroundingCT captures the full expressiveness of clinical language represented in free text and grounds it to spatially localized 3D segmentation annotations in volumetric imaging. This addresses a critical gap in medical AI: the abilit…

Breaking the Barriers of Text-Hungry and Audio-Deficient AI
Hamidou Tembine, Issa Bamia, Massa NDong, Bakary Coulibaly, Oumar Issiaka Traore, Moussa Traore, Moussa Sanogo, Mamadou Eric Sangare, Salif Kante, Daryl Noupa Yongueng, Hafiz Tiomoko Ali, Malik Tiomoko, Frejus Laleye, Boualem Djehiche, Wesmanegda Elisee Dipama, Idris Baba Saje, Hammid Mohammed Ibrahim, Moumini Sanogo, Marie Coursel Nininahazwe, Abdul-Latif Siita, Haine Mhlongo, Teddy Nelvy Dieu Merci Kouka, Mariam Serine Jerid…

Breaking the Barriers of Text-Hungry and Audio-Deficient AI
While global linguistic diversity spans more than 7164 recognized languages, the current dominant architecture of machine intelligence remains fundamentally biased toward written text. This bias excludes over 700 million people particularly in rural and remote regions who are audio-literate. In this work, we introduce a fully textless, audio-to-audio machine intelligence framework designed to serve this underserved population, and all the people who prefer audio-efficiency. Our contributions in…

BAGELS: Benchmarking the Automated Generation and Extraction of Limitations from Scholarly Text
Ibrahim Al Azher, Miftahul Jannat Mokarrama, Zhishuai Guo, Sagnik Ray Choudhury, Hamed Alhoori
https://arxiv.org/abs/2505.18207

BAGELS: Benchmarking the Automated Generation and Extraction of Limitations from Scholarly Text
In scientific research, limitations refer to the shortcomings, constraints, or weaknesses within a study. Transparent reporting of such limitations can enhance the quality and reproducibility of research and improve public trust in science. However, authors often a) underreport them in the paper text and b) use hedging strategies to satisfy editorial requirements at the cost of readers' clarity and confidence. This underreporting behavior, along with an explosion in the number of publications, …

Specialised or Generic? Tokenization Choices for Radiology Language Models
Hermione Warr, Wentian Xu, Harry Anthony, Yasin Ibrahim, Daniel McGowan, Konstantinos Kamnitsas
https://arxiv.org/abs/2508.09952

Specialised or Generic? Tokenization Choices for Radiology Language Models
The vocabulary used by language models (LM) - defined by the tokenizer - plays a key role in text generation quality. However, its impact remains under-explored in radiology. In this work, we address this gap by systematically comparing general, medical, and domain-specific tokenizers on the task of radiology report summarisation across three imaging modalities. We also investigate scenarios with and without LM pre-training on PubMed abstracts. Our findings demonstrate that medical and domain-s…

Benchmarking Neural Speech Codec Intelligibility with SITool
Anna Leschanowsky, Kishor Kayyar Lakshminarayana, Anjana Rajasekhar, Lyonel Behringer, Ibrahim Kilinc, Guillaume Fuchs, Emanu\"el A. P. Habets
https://arxiv.org/abs/2506.01731

Benchmarking Neural Speech Codec Intelligibility with SITool
Speech intelligibility assessment is essential for evaluating neural speech codecs, yet most evaluation efforts focus on overall quality rather than intelligibility. Only a few publicly available tools exist for conducting standardized intelligibility tests, like the Diagnostic Rhyme Test (DRT) and Modified Rhyme Test (MRT). We introduce the Speech Intelligibility Toolkit for Subjective Evaluation (SITool), a Flask-based web application for conducting DRT and MRT in laboratory and crowdsourcing…