arXiv AI By Swapnil Kundu Argha, Abdullah Al Shafi, Rowzatul Zannat, Shoumik Barman Polok, Abdul Muntakim, Jannatul Ferdousi, M. A. Moyeen

Bangla Sentence Function Classification: Corpus Development, Model Benchmarking, and Interpretability

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arXiv Computation and Language
Sep 22

Cross-Dialect NER for Bangla Regional Dialects Using Leave-One-Dialect-Out Cross-Validation and Explainable AI

The paper introduces a cross-dialect Named Entity Recognition (NER) framework for Bangla, leveraging the ANCHOLIK-NER dataset that covers five major regional dialects. Using a Leave-One-Dialect-Out Cross-Validation strategy, eight transformer-based models were evaluated, with Multilingual-E5 Large achieving the best performance (F1 up to 97.26% on Mymensingh, 82.38% on Chattogram). Local Interpretable Model-agnostic Explanations (LIME) revealed that the models rely mainly on the surface form of entity words rather than surrounding context, suggesting a direction for future improvement.

By Shamim Rahim Refat, Faika Fairuj Preotee, Shuvashis Sarker, Shifat Islam, Bidyarthi Paul, Mohammad Ashraful Hoque
arXiv Computation and Language
Sep 4

To What Extent Do Large Language Models Understand Bangla Idioms?

The paper introduces the first large‑scale benchmark dataset of Bangla idioms, along with a synthetic multiple‑choice question set for idiom meaning identification. It evaluates recent large language models on three idiom‑related tasks—paraphrasing, idiom span detection, and meaning identification—using zero‑shot and few‑shot prompting. Results show significant variability across models, with Phi‑4‑mini‑instruct best at paraphrasing, Kimi‑K2‑32b‑instruct excelling at span detection, and Gemini‑2.5‑flash leading in meaning identification.

By Mousumi Akter, Md. Faiyaz Abdullah Sayeedi, Nurul Labib Sayeedi, Swakkhar Shatabda
arXiv Computation and Language
3d ago

Text Corpora as Concept Fields: Providing Explainable Groundedness

The paper introduces the Concept Field, a local drift field with pointwise uncertainty derived from sentence-embedding deltas in a text corpus. It scores candidate sentence transitions using the mean absolute z-distance (ζ) to quantify groundedness, offering a model‑agnostic, corpus‑attributable metric. The authors implement a Vector Sequence Database to store embeddings and delta metadata, and demonstrate the method’s effectiveness in detecting groundedness in the U.S. Code of Federal Regulations and novelty in Project Gutenberg, achieving strong selective classification performance under a triage policy.

By Nicholas S. Kersting, Vittorio Castelli, Chieh Ting Yeh, Xinzhu Wang, Saad Taame, Khaoula Allak
arXiv AI
Sep 25

An Explainable DistilBERT-BiLSTM-Attention Framework for Binary and Multi-Class Hate Speech Detection

The paper presents an explainable hate‑speech detection framework that combines DistilBERT embeddings, a Bi‑LSTM network, and an attention mechanism to capture contextual and sequential information. It uses LIME to highlight influential text features, providing transparency in predictions. Evaluated on two benchmark datasets for both binary and multi‑class tasks, the model achieves F1‑scores of 96.78%–99.53% for binary classification and 94.99%–97.00% for multi‑class classification, outperforming existing baselines.

By Rameesha Zia, Muhammad Shahid Iqbal Malik