Context-Aware Stance Detection in Manipuri Editorials via FastText Embeddings and Keyword-Guided BiLSTM

Rationale-Guided Knowledge Distillation for Cross-Lingual Stance Detection

A rationale-guided knowledge distillation framework for cross-lingual stance detection using Chain-of-Thought prompting to guide Large Language Models in generating informative rationales, and distill the resulting reasoning knowledge into a compact student model.

Improving few-shot stance detection with MINE: meta-prompting and in-context N-shot examples

Stance detection aims to identify an author’s stance toward a target topic. Although recent prompting-based methods enable zero- and few-shot stance detection with large language models (LLMs), they often require extensive prompting or costly use of multiple LLM agents, as well as large labeled training datasets. We present MINE (Meta-prompting and In-context N-shot Examples), a simple, light-weight, training-free framework that first creates a target-specific LLM stance agent through meta-prompting, and then augments it with a small number of semantically retrieved examples. While meta-prompting and retrieval-augmented in-context learning have been studied independently, these components have not been combined specifically for stance detection in a training-free pipeline to the best of our knowledge. We also provide a systematic empirical study that quantifies the effects of the example retrieval strategy, sentence encoder choice, demonstrator pool size, and the number of examples on the performance of this hybrid methodology. We show that MINE achieves competitive performance when deployed as either a zero-shot or few-shot stance detector, matching or exceeding existing prompting-only approaches on SemEval-2016 and P-Stance while using fewer prompts and demonstrations. MINE remains effective with minimal labeled data (e.g., 10% of the training demonstrator pool) and generalizes to related tasks, including aspect-based sentiment analysis and cross-target stance detection.

Compact Models for Structured Argument and Stance Analysis: A Framing-Aware, Retrieval-Augmented Pipeline

We propose a modular, retrieval-augmented pipeline for computational argumentation that integrates two complementary components: ArgStance, a multi-task model for argument and stance reasoning, and TargetMatch, a contrastive retrieval model that treats target identification as a first-class retrieval task. We further formulate stance detection as a framing-aware problem, recognizing that the polarity of a stance toward a target depends on how the proposition is framed. To support broad generalization, we construct a large dataset spanning Kialo discussions, Wikipedia, and curated news articles, and introduce a cross-source injection strategy that mitigates domain and style biases. Our compact models achieve F1 scores of 0.94 for argument detection (ModernBERT-base) and 0.84 for same-side stance detection (ModernBERT-large), while TargetMatch attains a top-10 retrieval accuracy of 0.75. Under controlled zero-shot comparisons with large language models, our models remain competitive while offering advantages in reproducibility, deployment cost, and controllable intermediate predictions.

Negative focus detection via hypergraph attention LSTM network and LLM-driven data augmentation

Enhancing Fake News Detection in Low-Resource Linguistic Contexts using Translation-based NER and Lightweight NLI

A robust preprocessing pipeline incorporating Google Translation, spaCy-based NER with hyphenated-word normalization, and a future-event-aware fallback logic is proposed, achieving a 40 percent reduction in inference latency and closing the linguistic generalization gap left by previous works.

Use BiLSTM with Attention Mechanism to Optimize the Accuracy of Word Meaning Correspondence in Technical Texts

A robust WSD model that integrates a bidirectional long shortterm memory network (BiLSTM) with an attention mechanism, specifically designed for Chinese patent texts is proposed, providing reliable support for knowledge mining and intelligent text processing in technical domains.