3arXiv cs.CL (Computation and Language)·
SalAngaBhava: New Sinhala aspect-based sentiment analysis dataset for low-resource NLP
Researchers introduce SalAngaBhava, a manually annotated dataset of Sinhala product reviews labeled with aspect terms and associated sentiments (positive, negative, neutral) for aspect-based sentiment analysis (ABSA). The dataset addresses a gap in low-resource language NLP, specifically for Sinhala, an Indo-Aryan language primarily spoken in Sri Lanka. It is intended to serve as a benchmark for ABSA research and broader Sinhala NLP tasks.
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SetFitABSA: Few-Shot Aspect Based Sentiment Analysis using SetFit
Hugging Face introduces SetFitABSA, an extension of the SetFit few-shot learning framework for Aspect-Based Sentiment Analysis (ABSA). The approach enables fine-grained sentiment classification at the aspect level with minimal labeled data. This builds on SetFit's contrastive sentence-transformer training paradigm, adapting it to the structured ABSA task of identifying sentiment toward specific aspects within text.
CAVE-ABSA: Constraint-aware counterfactual editing framework for aspect-based sentiment analysis
Researchers introduce CAVE-ABSA, a framework for generating and validating aspect-level counterfactuals in Aspect-Based Sentiment Analysis (ABSA). The system localizes opinion spans, performs controlled rewriting, and filters candidates using semantic similarity, AMR-guided structural preservation, fluency, and contradiction detection. The framework targets a known weakness in counterfactual evaluation — existing methods flip sentence-level labels but may violate aspect-level constraints — and is designed to support both robustness evaluation and data augmentation for ABSA models.
L3Cube-MahaPOS: Gold-standard POS tagging dataset and BERT models for Marathi
Researchers introduce L3Cube-MahaPOS, a manually annotated part-of-speech tagging dataset for Marathi comprising 32,354 sentences drawn from news text, using a 16-tag Universal Dependencies-aligned scheme. The work benchmarks six model families including HMM, CRF, BiLSTM variants, MuRIL, and the Marathi-specific transformer MahaBERT-v2, with the best system achieving 88.67% token-level accuracy and 81.67% macro-F1. The dataset, annotation guidelines, and model checkpoints are released publicly to support further research in a severely under-resourced language spoken by over 83 million people.
Temporal Simultaneity Predicts Annotation Quality in Setswana Sentiment Corpora
Researchers present a Setswana sentiment dataset of 3,565 tweets annotated by three native speakers across eight batches, finding that inter-annotator agreement (IAA) declines sharply over time despite an aggregate Kappa of 0.76. The dominant predictor of agreement quality is temporal simultaneity: tweets labeled within one minute achieve κ=0.98 versus κ=0.65 for those labeled more than a day apart. The study also benchmarks multilingual encoders and proprietary models including GPT-5 and Gemini on three-class sentiment classification, with GPT-5 few-shot achieving the best result at 62.2 macro-F1. The dataset, timestamps, and analysis code are released to support reproducible quality auditing for African language NLP.
WhoSaidIt: Human-LLM Collaborative Annotation for Multilingual Speaker-Attribute Classification
This paper proposes a human-LLM collaborative re-annotation framework for stabilizing noisy multilingual speaker-attribute labels under resource constraints. LLMs surface recurring annotation rationales through iterative expert interaction, combined with disagreement-focused sampling for targeted re-annotation. The resulting WhoSaidIt dataset covers nine speaker-attribute labels across multiple languages. Benchmarking of recent LLMs reveals substantial cross-lingual annotation divergence and highlights both capabilities and limitations of LLMs in this classification task.
AI-PAVE-Br: LLM-based product attribute extraction system and Portuguese benchmark dataset for Brazilian e-commerce
Researchers introduce AI-PAVE-Br, an LLM-based system for Product Attribute Value Extraction (PAVE) tailored to Brazilian e-commerce catalogs in Portuguese. The paper also releases the Golden Set, a manually annotated benchmark dataset for PAVE in Portuguese, structured with entity, category, and subcategory annotations. Experiments show AI-PAVE-Br with prompt engineering substantially outperforms conventional NER baselines. The work addresses a gap in non-English NLP resources for structured e-commerce data extraction.
Symbal: Automated detection of systematic misalignments in MLLM-generated image captions
Researchers from Stanford introduce Symbal, a dual-stage framework using off-the-shelf foundation models to detect recurring, visually-correlated errors in multimodal LLM-generated captions. They also release SymbalBench, a benchmark of 1.7 million image-text pairs across 420 vision-language datasets covering natural and medical imaging domains. Symbal correctly identifies systematic misalignments in 63.8% of datasets, nearly 4x better than the closest baseline, and works without access to the underlying MLLM. The work addresses a practical data-quality auditing problem relevant to anyone building or evaluating vision-language datasets.
Unsupervised Sentiment Neuron
OpenAI researchers trained a character-level language model on Amazon reviews to predict the next character and discovered it spontaneously learned a single neuron encoding sentiment with high accuracy. The system achieved state-of-the-art sentiment classification with minimal labeled data, demonstrating that unsupervised language modeling can yield interpretable, task-relevant representations. This was an early result connecting unsupervised pretraining to downstream NLP tasks.