India

Entity-level sentiment analysis is becoming increasingly important in the context of diverse financial texts, and large language models demonstrate significant potential under zero-shot settings. While it is well recognized that different languages embody distinct cognitive patterns, the use of multilingual capabilities in large language models to enable cross-lingual collaborative reasoning in the financial domain remains insufficiently studied. To address this, we propose a Cross-Lingual Collaboration (CLC) method: first, financial texts are aligned from one language to another based on semantic and syntactic structures, enabling the model to capture complementary linguistic features. Then, we integrate sentiment analysis results from both languages through redundancy removal and conflict resolution, enhancing the effectiveness of cross-lingual collaboration. Our experiments cover seven languages from three language families, including six UN official languages, and evaluate CLC on two English datasets and one Chinese dataset. Results show that multilingual collaboration improves sentiment analysis accuracy, especially among linguistically similar languages. Furthermore, stronger reasoning capabilities in LLMs amplify these benefits. Our code is available at https://anonymous.4open.science/r/Cross-lingual-Collaboration.

IJCNLP-AACL 2025

EFSA-CLC: Enhancing Zero-shot Entity-level Financial Sentiment Analysis with Cross-lingual Collaboration

cross-lingual collaborative

entity-level sentiment analysis

large language models

### 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.

Temporal question answering (TQA) remains a persistent challenge for large language models (LLMs), particularly in retrieval-augmented generation (RAG) settings where retrieved content may be irrelevant, outdated, or temporally inconsistent. This is especially critical in applications like clinical event ordering, policy tracking, and real-time decision-making, which require reliable temporal reasoning even under noisy or misleading context. To address this challenge, we introduce RASTeR: Robust, Agentic, and Structured, Temporal Reasoning, an agentic prompting framework that separates context evaluation from answer generation. RASTeR first assesses the relevance and temporal coherence of retrieved context, then constructs a structured temporal knowledge graph (TKG) to better facilitate reasoning. When inconsistencies are detected, RASTeR selectively corrects or discards context before generating an answer. Across multiple datasets and LLMs, RASTeR consistently improves robustness: defined here as the model's ability to generate correct predictions despite suboptimal context. We further validate our approach through a ``needle-in-the-haystack'' study, in which relevant context is buried among irrelevant distractors. Even with forty distractors, RASTeR achieves 75% accuracy, compared to the runner-up model, which reaches only 62%.

RASTeR: Robust, Agentic, and Structured Temporal Reasoning

Pretraining on massive corpora has given rise to large language models (LLMs) with multi-task capabilities. However, real-world applications often require more specialized training, as is the case of NL2Code. We approach this specialization through the lens of data selection, i.e., identifying a subset of a large corpus that aligns with a desired target distribution---a challenge that remains under-explored within NL2Code. Existing methods are typically designed for selecting instruction-tuning data, and might not easily scale to large-scale code repositories; while methods for NL2Code do exist, they primarily rely on coarse heuristics—--such as repo stars—--for filtering. To bridge this gap, we propose FINDR, an efficient data selection method that extends logistic regression with feature-wise importance reweighting---marking it, to our knowledge, the first fine-grained solution to NL2Code pretraining. Our method uses hashed n-grams and code-aware features to capture code-specific patterns, and then apply informative priors to reweight feature importance when computing influence scores. Extensive experiments on NL2Python and NL2SQL, with two model families, show that FINDR consistently outperforms strong baselines in both execution accuracy and token efficiency. Notably, pretraining on only 2% of FINDR-selected data boosts Gemma by over 29% in both domains, even surpassing CodeGemma (pretrained on 300x more examples) by 10% in Python.

FINDR: A Fast Influential Data Selector for NL2Code Pretraining

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.

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

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.

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RASTeR: Robust, Agentic, and Structured Temporal Reasoning

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