India

Nigeria is the most populous country in Africa with a population of more than 200 million people. More than 500 languages are spoken in Nigeria and it is one of the most linguistically diverse countries in the world. Despite this, natural language processing (NLP) research has mostly focused on the following four languages: Hausa, Igbo, Nigerian-Pidgin, and Yoruba (i.e &lt;1% of the languages spoken in Nigeria). This is in part due to the unavailability of textual data in these languages to train and apply NLP algorithms.  In this work, we introduce Ibom---a dataset for machine translation and topic classification in four Coastal Nigerian languages from the Akwa Ibom State region: Anaang, Efik, Ibibio, and Oro. These languages are not represented in Google Translate or in major benchmarks such as Flores-200 or SIB-200. We focus on extending Flores-200 benchmark to these languages, and further align the translated texts with topic labels based on SIB-200 classification dataset. Our evaluation shows that current LLMs perform poorly on machine translation for these languages in both zero-and-few shot settings. However, we find the few-shot samples to steadily improve topic classification with more shots.

IJCNLP-AACL 2025

Ibom NLP: A Step Toward Inclusive Natural Language Processing for Nigeria’s Minority Languages

low resource langauges

nigeria

machine translation

Nigeria is the most populous country in Africa with a population of more than 200 million people. More than 500 languages are spoken in Nigeria and it is one of the most linguistically diverse countries in the world. Despite this, natural language processing (NLP) research has mostly focused on the following four languages: Hausa, Igbo, Nigerian-Pidgin, and Yoruba (i.e <1% of the languages spoken in Nigeria). This is in part due to the unavailability of textual data in these languages to train and apply NLP algorithms. In this work, we introduce Ibom---a dataset for machine translation and topic classification in four Coastal Nigerian languages from the Akwa Ibom State region: Anaang, Efik, Ibibio, and Oro. These languages are not represented in Google Translate or in major benchmarks such as Flores-200 or SIB-200. We focus on extending Flores-200 benchmark to these languages, and further align the translated texts with topic labels based on SIB-200 classification dataset. Our evaluation shows that current LLMs perform poorly on machine translation for these languages in both zero-and-few shot settings. However, we find the few-shot samples to steadily improve topic classification with more shots.

### Welcome to IJCNLP-AACL 2025! 
 It is a great honor to host this joint conference in Mumbai, India, from December 20 to 24, 2025. The joint conferences of IJCNLP and AACL are organized with alternating leadership in the Asia-Pacific region. The event is run by the Asian Federation of Natural Language Processing (AFNLP) in odd years, and by AACL in even years, while it is organized solely by ACL when the annual ACL meeting is held in the region. This year, the conference is primarily organized by AFNLP. 
*Kentaro Inui
MBZUAI, UAE
General Chair, IJCNLP-AACL 2025* 
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The 14th IJCNLP & 4th AACL will be held in Mumbai, India from December 20th to December 24th, 2025.

The translation of idiomatic expressions often results in misunderstandings and inaccuracies, affecting everyday communication as well as machine translation systems. This paper introduces Idiom-aware Vietnamese Translation (IDiAT), a new framework for the evaluation of idiomatic translation for Vietnamese, along with state-of-the-art results for this task. We collect and curate a high-quality Vietnamese-English idiom set that serves as a resource for in-context learning (ICL). IDiAT's evaluation benchmark includes both idiomatic and non-idiomatic text pairs to assess general translation quality and idiomatic translation performance. We leverage ICL in large language models to augment few-shot demonstrations with idiom and topic descriptions and consequently improve the translation accuracy. Empirical results demonstrate that our IDiAT-based ICL outperforms traditional supervised methods using only a few data samples. Multiple evaluations confirm the effectiveness of our proposed approach. Though focusing on the Vietnamese language, our approach advances idiomatic translation and contributes to the development of culturally aware translation systems, paving the way for future research in low-resource languages. The experimental materials are publicly available.

Don't Take it Literally! Idiom-aware Vietnamese Translation via In-context Learning

Question answering in low-resource languages faces critical challenges when models encounter questions beyond their knowledge boundaries, often producing confident but incorrect answers. We propose Knowledge-Enhanced Reinforcement Learning for Question Answering (KERLQA), a novel approach that combines knowledge graph integration with reinforcement learning to enable principled abstention decisions. Unlike existing refusal-tuned methods that make binary decisions based solely on internal confidence, KERLQA implements a three-way decision process: answer with internal knowledge, answer with external knowledge assistance, or abstain. Using a composite reward function that jointly optimizes for correctness, appropriate abstention, and efficient knowledge utilization, we train policies via PPO and DPO with dynamic calibration for low-resource settings. Experiments on CommonsenseQA and OpenBookQA across English and four South African languages show KERLQA achieves improved F1 scores, with up to 6.2 point improvements in low-resource languages. Our analysis reveals that KERLQA reduces false positive abstention rates by 30% while expanding the boundary of answerable questions through external knowledge integration.

KERLQA: Knowledge-Enhanced Reinforcement Learning for Question Answering in Low-resource Languages

Realignment is a promising strategy to improve cross-lingual transfer in multilingual language models. However, empirical results are mixed and often unreliable, particularly for typologically distant or low-resource languages (LRLs) compared to English. Moreover, word realignment tools often rely on high-quality parallel data, which can be scarce or noisy for many LRLs. In this work, we conduct an extensive empirical study to investigate whether realignment truly benefits from using all available languages, or if strategically selected subsets can offer comparable or even improved cross-lingual transfer, and study the impact on LRLs. Our controlled experiments show that realignment can be particularly effective for LRLs and that using carefully selected, linguistically diverse subsets can match full multilingual alignment, and even outperform it for unseen LRLs. This indicates that effective realignment does not require exhaustive language coverage and can reduce data collection overhead, while remaining both efficient and robust when guided by informed language selection.

Rethinking what matters: Effective and Robust Multilingual Realignment for Low-Resource Languages

Limited data for low-resource languages typically yields weaker language models (LMs). Since pre-training is compute-intensive, it is more pragmatic to target improvements during fine-tuning. In this work, we examine the use of Active Learning (AL) methods augmented by structured data selection strategies across epochs, which we term 'Active Learning schedulers,' to boost the fine-tuning process with a limited amount of training data. We connect the AL process to data clustering and propose an integrated fine-tuning pipeline that systematically combines AL, data clustering, and dynamic data selection schedulers to enhance models' performance. Several experiments on the Slovak, Maltese, Icelandic, and Turkish languages show that the use of clustering during the fine-tuning phase together with novel AL scheduling can for models simultaneously yield annotation savings up to 30% and performance improvements up to four F1 score points, while also providing better fine-tuning stability.

Enhancing BERT Fine-Tuning for Sentiment Analysis in Lower-Resourced Languages

Multimodal emotion recognition (MER) plays a crucial role in human-centric AI applications, yet existing models struggle in low-resource scenarios due to their heavy reliance on large amounts of high-quality labeled data. To address this challenge, we propose Adaptive Collaborative Labeling for Low-Resource MER (ACL-MER), a novel framework that leverages off-the-shelf multimodal large language models (MLLMs) to effectively exploit abundant unlabeled data. Specifically, ACL-MER incorporates a diverse teacher model zoo, wherein each MLLM specializes in a specific modality and is prompted to generate chain-of-thought predictions accompanied by scalar confidence scores. Rather than directly adopting these pseudo-labels, ACL-MER introduces an adaptive refinement strategy that selectively distills knowledge based on teacher confidence, iteratively guiding the lightweight student model toward robust learning under limited supervision. Extensive experiments on two benchmarks demonstrate that ACL-MER consistently outperforms strong baselines, especially in extremely low-resource settings.

Adaptive Collaborative Labeling with MLLMs for Low-Resource Multimodal Emotion Recognition

Retrieval-augmented generation (RAG) still *forwards* raw passages to large-language models, so private facts slip through. Prior defenses are either (i) **heavyweight**—full DP training that is impractical for today’s 70B-parameter models—or (ii) **over-zealous**—blanket redaction of every named entity, which slashes answer quality.

We introduce **VAGUE-Gate**, a lightweight, *locally* differentially-private gate deployable in front of *any* RAG system. A precision pass drops low-utility tokens under a user budget ε, then up to k(ε) high-temperature paraphrase passes further cloud residual cues; post-processing guarantees preserve the same ε-LDP bound.

To measure both privacy and utility, we release **BlendPriv** (3k blended-sensitivity QA pairs) and two new metrics: a lexical Information-Leakage Score and an LLM-as-Judge score. Across eight pipelines and four SOTA LLMs, **VAGUE-Gate** at ε = 0.3 lowers lexical leakage by **70%** and semantic leakage by **1.8** points (1–5 scale) while retaining **91%** of Plain-RAG faithfulness with only a **240 ms** latency overhead.

All code, data, and prompts are publicly released:

- Code: < https://github.com/arshiahemmat/LDP_RAG > 
- Dataset: <https://huggingface.co/datasets/AliMnp/BlendPriv>

VAGUE‑Gate: Plug‑and‑Play Local‑Privacy Shield for Retrieval‑Augmented Generation

Recent work in Differential Privacy with Natural Language Processing (DP NLP) has proposed numerous promising techniques in the form of *text rewriting* mechanisms. In the evaluation of these mechanisms, an often-ignored aspect is that of *dataset size*, or rather, the effect of dataset size on a mechanism's efficacy for utility and privacy preservation. In this work, we are the first to introduce this factor in the evaluation of DP text privatization, where we design utility and privacy tests on large-scale datasets with dynamic split sizes. We run these tests on datasets of varying size with up to one million texts, and we focus on quantifying the effect of increasing dataset size on the privacy-utility trade-off. Our findings reveal that dataset size plays an integral part in evaluating DP text rewriting mechanisms; additionally, these findings call for more rigorous evaluation procedures in DP NLP, as well as shed light on the future of DP NLP in practice and at scale.

With Privacy, Size Matters: On the Importance of Dataset Size in Differentially Private Text Rewriting

Human behaviors are often guided or constrained by social norms, which are defined as shared, commonsense rules. For example, underlying an action report a witnessed crime are social norms that inform our conduct, such as It is expected to be brave to report crimes. Current AI systems that assess valence (i.e., support or oppose) of human actions by leveraging large-scale data training not grounded on explicit norms may be difficult to explain, and thus untrustworthy. Emulating human assessors by considering social norms can help AI models better understand and predict valence. While multiple norms come into play, conflicting norms can create tension and directly influence human behavior. For example, when deciding whether to report a witnessed crime, one may balance bravery against self-protection. In this paper, we introduce ClarityEthic, a novel ethical assessment approach, to enhance valence prediction and explanation by generating conflicting social norms behind human actions, which strengthens the moral reasoning capabilities of language models by using a contrastive learning strategy. Extensive experiments demonstrate that our method outperforms strong baseline approaches, and human evaluations confirm that the generated social norms provide plausible explanations for the assessment of human behaviors.

Explainable Ethical Assessment on Human Behaviors by Generating Conflicting Social Norms

Large language models (LLMs) are increasingly being deployed in high-stakes applications like hiring, yet their potential for unfair decision-making remains understudied in generative and retrieval settings. In this work, we examine the allocational fairness of LLM-based hiring systems through two tasks that reflect actual HR usage: resume summarization and applicant ranking. By constructing a synthetic resume dataset with controlled perturbations and curating job postings, we investigate whether model behavior differs across demographic groups. Our findings reveal that generated summaries exhibit meaningful differences more frequently for race than for gender perturbations. Models also display non-uniform retrieval selection patterns across demographic groups and exhibit high ranking sensitivity to both gender and race perturbations. Surprisingly, retrieval models can show comparable sensitivity to both demographic and non-demographic changes, suggesting that fairness issues may stem from broader model brittleness. Overall, our results indicate that LLM-based hiring systems, especially in the retrieval stage, can exhibit notable biases that lead to discriminatory outcomes in real-world contexts.

Small Changes, Large Consequences: Analyzing the Allocational Fairness of LLMs in Hiring Contexts

Large language models (LLMs) are integrated into applications like shopping reviews, summarization, or medical diagnosis support, where their use affects human decisions. We investigate the extent to which LLMs expose users to biased content and demonstrate its effect on human decision-making. We assess five LLM families in summarization and news fact-checking tasks, evaluating the consistency of LLMs with their context and their tendency to hallucinate on a new self-updating dataset. Our findings show that LLMs expose users to content that changes the context’s sentiment in 26.42% of cases (framing bias), hallucinate on 60.33% of post-knowledge-cutoff questions, and highlight context from earlier parts of the prompt (primacy bias) in 10.12% of cases, averaged across all tested models. We further find that humans are 32% more likely to purchase the same product after reading a summary of the review generated by an LLM rather than the original review. To address these issues, we evaluate 18 mitigation methods across three LLM families and find the effectiveness of targeted interventions.

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Don't Take it Literally! Idiom-aware Vietnamese Translation via In-context Learning

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