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MASTE: A Multi-Agent Pipeline for Zero-Shot Aspect Sentiment Triplet Extraction

AI·MASTE: A Multi-Agent Pipeline for Zero-Shot Aspect Sentiment Triplet Extraction

When Debiasing Backfires: Counterintuitive Side Effects of Preprocessing-Based Stereotype Mitigation

AI·The research finds that preprocessing-based stereotype mitigation in NLP can backfire by increasing stereotyping or counter-stereotyping for some groups relative to neutral baselines.

COALA: Robust Contextualized Speech-augmented Language Modeling for ASR via Contrastive Regularizer and Biasing Score Estimation

AI·COALA enhances contextualized SLMs for multi-entity ASR via contrastive regularizer and biasing score estimation to handle domain-specific entities robustly.

COBART: Controlled, Optimized, Bidirectional and Auto-Regressive Transformer for Ad Headline Generation

AI·COBART uses a bidirectional auto-regressive transformer for optimized ad headline generation with multi-objective control over quality and CTR.

Unveiling Public Opinion: A Study of Sentiment Analysis Using LSTM and Traditional Models

AI·The study applies LSTM and traditional models to analyze public sentiment on social media platforms like Twitter regarding real-time events and issues.

Echoes Across Vietnam's Highlands, Delta, and Coast: A Multilingual Corpus for Cham, Khmer, and Tay-Nung

AI·CKTN multilingual corpus covers Cham, Khmer, and Tay-Nung from Vietnam's highlands, delta, and coast for NLP of under-resourced minority languages.

Withdraw from ACL ARR and resubmit to a workshop? [D]

AI·User questions whether to withdraw from ACL ARR and resubmit to a workshop after mediocre 2.5-3 scores on an Interpretability-track EMNLP paper.

First time ARR users - some questions [D]

We submitted our first paper to ARR, intending to commit to IJCNLP-AACL. Area: Multilingualism and Cross-Lingual NLP Scores: (3,4) (2.5,3) (3,3) - average 2.83 for reviews, 3.33 for confidence 3 for soundness on all, 4 for reproducibility, and 2,3,3 for excitement. The reviewer who gave us 2.5 has a very short review. They only list one weakness in two sentences and give the paper 2.5. They also …

PAST-TIDE: Prototype-Anchored Statement Tuning with Topic-Invariant Normalization for Stance Detection

We introduce PAST-TIDE, our stance detection system addressing both subtasks of the StanceNakba Shared Task at NakbaNLP@LREC-COLING 2026. The main idea is statement tuning. We redefine stance as cloze-style masked language modeling (MLM), letting a verbalizer map label words to stance categories through the pre-trained MLM head rather than appending a randomly initialized classification head. We complement this with prototypical contrastive learning, which uses learnable class prototypes for ba…

Can Dialects Be Steered Like Languages? Sparse Neurons and Distributed Directions in Arabic LLMs

A key challenge in Arabic NLP is the scarcity of dialectal data relative to Modern Standard Arabic (MSA), causing LLMs to overproduce MSA and struggle with dialectally accurate generation. From an interpretability perspective, this raises a fundamental question: where and how are dialectal features encoded within model internals, and can these representations be leveraged to improve dialect generation without fine-tuning? This study investigates two complementary inference-time approaches that …

RuleChef: Grounding LLM Task Knowledge in Human-Editable Rules

We present RuleChef, a framework that uses large language models (LLMs) to generate executable rules for NLP tasks such as text classification, Named Entity Recognition (NER), or relation extraction. Rules are generated based on a task description and a set of labeled examples, then they are iteratively improved based both on additional examples and on human feedback overexisting rules. RuleChef can also be used to bootstrap rules using the observed input-output pairs from any existing model fo…