India

Developing robust Natural Language Understanding (NLU) for morphologically rich Dravidian languages like Kannada, Malayalam, Tamil, and Telugu presents significant challenges due to their agglutinative nature and syntactic complexity. In this work, we present the Dravidian NLP Suite tackling five core tasks: Morphological Analysis (MA), POS Tagging (POS), Named Entity Recognition (NER), Dependency Parsing (DEP), and Coreference Resolution (CR), trained for monolingual models and multilingual models. To facilitate this, we present the Dravida dataset, meticulously annotated multilingual corpus for these tasks across all four languages. Our experiments demonstrate that a multilingual model, which utilizes shared linguistic features and cross-lingual patterns inherent to the Dravidian family, consistently outperforms its monolingual counterparts across all tasks. These findings suggest that multilingual learning is an effective approach for enhancing Natural Language Understanding (NLU) capabilities, particularly for languages belonging to the same family. To the best of our knowledge, this is the first work to jointly address all these core tasks on the Dravidian languages.

IJCNLP-AACL 2025

Family helps one another: Dravidian NLP suite for Natural Language Understanding

synthetic data preparation

coreference resolution

multilingual

poster

### 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* 
Read full message and download the Conference Handbook [**here**](https://drive.google.com/file/d/1UTwxkAqSqI-GAoJC3wE1zZt5VP1Y8GX0/view?usp=sharing).

To access this event page, you will need to log in with the **email address you registered with**. Access credentials will be sent to your email from Underline - subject line "Welcome to IJCNLP-AACL 2025". Please be sure to check your spam email folder if you do not see an email confirmation right away.

To access this event page, you are required to register.
Please complete your registration to continue.

We recommend reading [**the registration information**](https://2025.aaclnet.org/registration/) first.


The 14th IJCNLP & 4th AACL will be held in Mumbai, India from December 20th to December 24th, 2025.

Simultaneous speech translation (SiST) requires balancing translation quality and latency. While most SiST systems follow machine translation assumptions that prioritize full semantic accuracy to the source, human interpreters often omit less critical content to catch up with the speaker. This study investigates whether omission can be used to reduce latency while preserving meaning in SiST.We construct a dataset that includes omission using large language models (LLMs) and propose a Target-Duration Latency (TDL), target-based latency metric that measures the output length considering the start and end timing of translation. Our analysis shows that LLMs can omit less important words while retaining the core meaning. Furthermore, experimental results show that although standard metrics overlook the benefit of the model trained with proposed omission-involving dataset, alternative evaluation methods capture it, as omission leads to shorter outputs with acceptable quality.

Investigating Omission as a Latency Reduction Strategy in Simultaneous Speech Translation

Small, resource-efficient language models are pivotal for extending high-quality text generation to low-resource and regional languages—the true frontier of linguistic equity in AI. Yet research largely prioritises massive English-centric systems, leaving regional-centric (low-resource) language modelling underexplored, particularly how tokenizer design, dataset diversity, and linguistic structure shape the inference of Small Language Models (SLMs) under realistic computational and data constraints. We present Regional-TinyStories, a lightweight framework that treats SLMs as cost-effective stand-ins for LLMs, enabling rapid, variable-wise inference-based analysis. Extending TinyStories to Hindi, Marathi, and Bangla, we release datasets of 2M synthetic and translated stories per language and train over 20 SLMs spanning 5–157M parameters. Using this framework, we (i) uncover contrasts between form-oriented (grammar, fluency) and content-oriented (context, completeness, creativity) metrics; (ii) chart language-specific learning dynamics; (iii) rank tokenizers, showing Indic-specific Sarvam-1 outperforming SUTRA and generic Tiktoken (GPT-2) across all metrics; and (iv) demonstrate that dataset semantic quality (translation vs. synthetic) strongly governs downstream generation. Validation through an LLM-as-Judge ensemble (GPT-4o, LLaMA-3.3-70B) and a 100+ participant human study confirms these trends while exposing systematic score inflation in automated evaluations. Regional-TinyStories offers a reproducible path to benchmark tokenizers, datasets, and SLM designs for scalable, context-faithful generation in low-resource settings.

Regional-TinyStories: A Small Language Model Framework for Evaluating Language Learning, Tokenizers, and Datasets

Large language models (LLMs) have transformed machine translation, yet they have a high subword fertility issue for low-resource languages, which leads to slow inference speed and increased costs. While vocabulary expansion via continual pre-training is a common solution, it often degrades translation quality and requires large target-language corpora, which are unavailable for truly low-resource languages. To address this, we investigate tokenization efficiency through an information-theoretic lens, building on the established hypothesis that word length correlates with information content. From this perspective, we characterize tokenization inefficiency as having high fertility for low-information (highly predictable) words. Guided by this principle, we introduce a novel fine-tuning strategy that systematically identifies informationally redundant words—those with high fertility but low information content—for targeted vocabulary expansion and model fine-tuning. Experiments fine-tuning BLOOM and LLaMA-3 in English-Manipuri and other two language pairs show that our proposed method significantly reduces fertility by 50\% and accelerates inference by more than 2 times, without compromising and often exceeding the translation quality of standard LLM baselines, providing a theoretically grounded solution for efficient LLM-based MT.

An Information-Theoretic Approach to Reducing Fertility in LLMs for Manipuri Machine Translation

India, a country with a large population, possesses two official and twenty-two scheduled languages, making it the most linguistically diverse nation. Despite being one of the scheduled languages, Santali remains a low-resource language. Although Ol Chiki is recognized as the official script for Santali, many continue to use Bengali, Devanagari, Odia, and Roman scripts. In tribute to the upcoming centennial anniversary of the Ol Chiki script, we present an Automatic Speech Recognition for Santali in the Ol Chiki script. Our approach involves cross-lingual transfer learning by utilizing the Whisper framework pre-trained in Bengali and Hindi on the Santali language, using Ol Chiki script transcriptions. With the adoption of the Bengali pre-trained framework, we achieved a Word Error Rate (WER) score of 28.47%, whereas the adaptation of the Hindi pre-trained framework resulted in a score of 34.50% WER. These outcomes were obtained using the Whisper Small framework.

Whispering in Ol Chiki: Cross-Lingual Transfer Learning for Santali Speech Recognition

Recent studies have combined Large Language Models (LLMs) with Knowledge Graphs (KGs) to enhance reasoning, improving inference accuracy without additional training while mitigating hallucination. However, existing frameworks still suffer two practical drawbacks: they must be re-tuned whenever the KG or reasoning task changes, and they depend on a single, high-capacity LLM for reliable (i.e., trustworthy) reasoning. To address this, we introduce R2-KG, a plug-and-play, dual-agent framework that separates reasoning into two roles: an Operator (a low-capacity LLM) that gathers evidence and a Supervisor (a high-capacity LLM) that makes final judgments. This design is cost-efficient for LLM inference while still maintaining strong reasoning accuracy. Additionally, R2-KG employs an Abstention mechanism, generating answers only when sufficient evidence is collected from KG, which significantly enhances reliability. Experiments across five diverse benchmarks show that R2-KG consistently outperforms baselines in both accuracy and reliability, regardless of the inherent capability of LLMs used as the operator. Further experiments reveal that the single-agent version of R2-KG, equipped with a strict self-consistency strategy, achieves significantly higher-than-baseline reliability with reduced inference cost but increased abstention rate in complex KGs. Our findings establish R2-KG as a flexible and cost-effective solution for KG-based reasoning, reducing reliance on high-capacity LLMs while ensuring trustworthy inference.

R2-KG: General-Purpose Dual-Agent Framework for Reliable Reasoning on Knowledge Graphs

Full-duplex spoken dialogue models, which process audio input and output simultaneously, have been actively studied for their ability to naturally model turn-taking and non-verbal phenomena in addition to generating responses. Although these models enable natural conversational flow, they lack mechanisms for language understanding and dialogue management, making them difficult to apply to task-oriented dialogue systems. We propose a method for incorporating dialogue state tracking in task-oriented dialogue into Moshi, aiming to achieve a multi-channel, full-duplex task-oriented spoken dialogue model. We evaluated the proposed method on JMultiWOZ, a benchmark corpus for Japanese task-oriented dialogue, focusing on dialogue state tracking and response generation.

Incorporating Dialogue State Tracking into Japanese Full-duplex Task-oriented Spoken Dialogue Model

Dysarthria, a motor speech disorder affecting over 46 million individuals globally, impairs both intelligibility and emotional expression in communication. This work introduces a novel framework for emotion-aware sentence reconstruction from dysarthric speech using Large Language Models (LLMs) fine-tuned with QLoRA, namely LLaMA 3.1 and Mistral 8x7B. Our pipeline integrates direct emotion recognition from raw audio and conditions textual reconstruction on this emotional context to enhance both semantic and affective fidelity.

We propose the Multimodal Communication Dysarthria Score (MCDS), a holistic evaluation metric combining BLEU, semantic similarity, emotion consistency, and human ratings:

MCDS=αB+βE+γS+δH

where $\alpha + \beta + \gamma + \delta = 1$.

On our extended TORGO+ dataset, our emotion-aware LLM model achieves a MCDS of 0.87 and BLEU of 72.4%, significantly outperforming traditional pipelines like Kaldi GMM-HMM (MCDS: 0.52, BLEU: 38.1%) and Whisper-based models. It also surpasses baseline LLM systems by 0.09 MCDS. This sets a new benchmark in emotionally intelligent dysarthric speech reconstruction, with future directions including multilingual support and real-time deployment.

Emotion-Aware Dysarthric Speech Reconstruction: LLMs and Multimodal Evaluation with MCDS

The proliferation of educational videos on the Internet has changed the educational landscape by enabling students to learn complex concepts at their own pace. Our work outlines the vision of an automated tutor – a multimodal question answering (QA) system to answer questions from students watching a video. This can make doubt resolution faster and further improve learning experience. In this work, we take first steps towards building such a QA system. We curate and release a dataset named EduVidQA, with 3,158 videos and 18,474 QA-pairs. However, building and evaluating an educational QA system is challenging because (1) existing evaluation metrics do not correlate with human judgments, and (2) a student question could be answered in many different ways, training on a single gold answer could confuse the model and make it worse. We conclude with important research questions to develop this research area further.

Towards Multimodal Question Answering in Educational Domain

Punctuation plays a vital role in structuring meaning, yet current models often struggle to restore it accurately in transcripts of spontaneous speech, especially in the presence of disfluencies such as false starts and backtracking. These limitations hinder the performance of downstream tasks like translation, text-to-speech, summarization, etc. where sentence boundaries are critical for preserving quality. In this work, we introduce Cadence, a generalist punctuation restoration model adapted from a pretrained large language model. Cadence is designed to handle both clean written text and highly spontaneous spoken transcripts. It surpasses the previous state-of-the-art in performance while expanding support from 14 to all 22 Indian languages and English. We conduct a comprehensive analysis of model behavior across punctuation types and language families, identifying persistent challenges under domain shift and with rare punctuation marks. Our findings demonstrate the efficacy of utilizing pretrained language models for multilingual punctuation restoration and highlight Cadence’s practical value for low-resource NLP pipelines at scale.

Mark My Words: A Robust Multilingual Model for Punctuation in Text and Speech Transcripts

Response consistency-based, reference-free hallucination detection (RFHD) methods do not depend on internal model states, such as generation probabilities or gradients, which Grey-box models typically rely on but are inaccessible in closed-source LLMs. However, their inability to capture query-response alignment patterns often results in lower detection accuracy. Additionally, the lack of large-scale benchmark datasets spanning diverse domains remains a challenge, as most existing datasets are limited in size and scope. To this end, we propose HalluCounter, a novel reference-free hallucination detection method that utilizes both response-response and query-response consistency and alignment patterns. This enables the training of a classifier that detects hallucinations and provides a confidence score and an optimal response for user queries. Furthermore, we introduce HalluCounterEval, a benchmark dataset comprising both synthetically generated and human-curated samples across multiple domains. Our method outperforms state-of-the-art approaches by a significant margin, achieving over 90% average confidence in hallucination detection across datasets.

Downloads

Next from IJCNLP-AACL 2025

Investigating Omission as a Latency Reduction Strategy in Simultaneous Speech Translation

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES