India

We investigate communication emerging in noisy environments with the goal of capturing the impact of message disruption on the emerged protocols. We implement two different noise mechanisms, inspired by the erasure and deletion channels studied in information theory, and simulate a referential game in a neural agent-based model with a variable message length channel. We leverage a stochastic evaluation setting to apply noise only after a message is sampled, which adds ecological validity and allows us to estimate information-theoretic measures of the emerged protocol directly from symbol probabilities. Contrary to our expectations, the emerged protocols do not become more redundant with the presence of noise; instead, we observe that certain levels of noise encourage the sender to produce more compositional messages, although the impact varies depending on the type of noise and input representation.

IJCNLP-AACL 2025

Noise May Drown Out Words but Foster Compositionality: The Advantage of the Erasure and Deletion Noisy Channels on Emergent Communication

noisy channel

agent based modeling

language emergence

emergent communication

compositionality

### 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.

Recent advances in singing voice synthesis (SVS) have attracted substantial attention from both academia and industry. With the advent of large language models and novel generative paradigms, producing controllable, high‑fidelity singing voices has become an attainable goal. Yet the field still lacks a comprehensive survey that systematically analyzes deep‑learning‑based singing voice systems and their enabling technologies.
To address the aforementioned issue, this survey first categorizes existing systems by task type and then organizes current architectures into two major paradigms: cascaded and end-to-end approaches. Moreover, we provide an in-depth analysis of core technologies, covering singing modeling and control techniques. Finally, we review relevant datasets, annotation tools, and evaluation benchmarks that support training and assessment. In appendix, we introduce training strategies and further discussion of SVS. This survey provides an up-to-date review of the literature on SVS models, which would be a useful reference for both researchers and engineers. Related materials are available at https://github.com/David-Pigeon/SyntheticSingers.

Synthetic Singers: A Review of Deep-Learning-based Singing Voice Synthesis Approaches

Recent approaches have shown impressive proficiency in extracting and leveraging parametric knowledge from Large-Language Models (LLMs) and Vision-Language Models (VLMs). In this work, we consider how we can improve the retrieval of videos related to complex real-world events by automatically extracting latent parametric knowledge about those events. We present Q2E: a Query-to-Event decomposition method for zero-shot multilingual text-to-video retrieval, adaptable across datasets, domains, LLMs, or VLMs. Our approach demonstrates that we can enhance the understanding of otherwise overly simplified human queries by decomposing the query using the knowledge embedded in LLMs and VLMs. We additionally show how to apply our approach to both visual and speech-based inputs. To combine this varied multimodal knowledge, we adopt entropy-based fusion scoring for zero-shot fusion. Q2E outperforms the previous SOTA on the MultiVENT dataset by 8 NDCG points, while improving on MSR-VTT and MSVD by 4 and 3 points, respectively, outperforming several existing retrieval methods, including many fine-tuned and SOTA zero-shot approaches. We have released both code and data.

Q2E: Query-to-Event Decomposition for Zero-Shot Multilingual Text-to-Video Retrieval

Vision-Language Models (VLMs) often struggle to balance visual and textual information when summarizing complex multimodal inputs, such as entire TV show episodes. In this paper, we propose a zero-shot video-to-text summarization approach that builds its own screenplay-like representation of an episode, effectively integrating key video moments, dialogue, and character information into a unified document. Unlike previous approaches, we simultaneously generate screenplays and name the characters in zero-shot, using only the audio, video, and transcripts as input. Additionally, we highlight that existing summarization metrics can fail to assess the multimodal content in summaries. To address this, we introduce MFactSum, a multimodal metric that evaluates summaries with respect to both vision and text modalities. Using MFactSum, we evaluate our screenplay summaries on the SummScreen3D dataset, demonstrating superiority against state-of-the-art VLMs such as Gemini 1.5 by generating summaries containing 20% more relevant visual information while requiring 75% less of the video as input.

Integrating Video and Text: A Balanced Approach to Multimodal Summary Generation and Evaluation

Clinical trials are designed in natural language and the task of matching them to patients, represented via both structured and unstructured textual data, benefits from knowledge aggregation and reasoning abilities of LLMs. LLMs with their ability to consolidate distributed knowledge hold the potential to build a more general solution than classical approaches that employ trial-specific heuristics. Yet, adoption of LLMs in critical domains, such as clinical research, comes with many challenges, such as, the availability of public benchmarks, the dimensions of evaluation and data sensitivity. In this survey, we contextualize emerging LLM-based approaches in clinical trial recruitment. We examine the main components of the clinical trial recruitment process, discuss existing challenges in adopting LLM technologies in clinical research and exciting future directions.

A Survey on LLM-Assisted Clinical Trial Recruitment

The quality of training data is crucial for the performance of supervised machine learning models. In particular, poor annotation quality and spurious correlations between labels and features in text dataset can significantly degrade model generalization. This problem is especially pronounced in harmful language detection, where prior studies have revealed major deficiencies in existing datasets. In this work, we design and test data selection methods based on learnability measures to improve dataset quality. Using a sexism dataset with counterfactuals designed to avoid spurious correlations, we show that pruning with EL2N and PVI scores can lead to significant performance increases and outperforms submodular and random selection. Our analysis reveals that in presence of label imbalance models rely on dataset shortcuts; especially easy-to-classify sexist instances and hard-to-classify non-sexist instances contain shortcuts. Pruning these instances leads to performances increases. Pruning hard-to-classify instances is in general a promising strategy as well when shortcuts are not present.

Enhancing Training Data Quality through Influence Scores for Generalizable Classification: A Case Study on Sexism Detection

Personalization is critical for improving user experience in interactive writing and educational applications, yet remains understudied in story generation. We study the task of personalizing story generation, where our goal is to mimic an author's writing style, given other stories written by them. We collect Mythos, a dataset of 3.6k stories from 112 authors, with an average of 16 stories per author, across five distinct sources reflecting diverse story-writing settings. We propose a two-stage pipeline for personalized story generation: first, we infer authors' implicit writing characteristics and organize them into an Author Writing Sheet, which is validated by humans to be of high quality; second, we simulate the author's persona using tailored persona descriptions and personalized story rules. We find that stories personalized using the Author Writing Sheet outperform a non-personalized baseline, achieving a 78% win-rate in capturing authors' past style and 59% in similarity to ground-truth author stories. Human evaluation supports these findings and further highlights trends, such as Reddit stories being easier to personalize, and the Creativity and Language Use aspects of stories being easier to personalize than the Plot.

Whose story is it? Personalizing story generation by inferring author styles

Reference-free metrics such as CLIPScore and PAC-S are increasingly used in vision-language tasks due to their scalability and independence from human-written references. However, their reliability under linguistic, visual, and cultural variation remains underexplored. In this work, we present a systematic audit of CLIPScore and PAC-S using an eight-factor diagnostic framework applied to MS-COCO validation images. Our analysis reveals consistent failure modes across dimensions including object size, content category, syntax, named entities, spatial relations and cultural context. Both metrics penalize captions referencing African (−5.5%, −4.8%) and Arabian (−4.9%, −5.3%) cultures, favor large-object and animal-centric scenes (by 20-30%) and show limited sensitivity to spatial negation and word order. CLIPScore correlates more strongly with syntactic complexity, while PAC-S demonstrates greater robustness to verbosity and named–entity variation highlighting complementary strengths rather than superiority. These findings expose cultural and content bias, weak semantic robustness, and limited compositional understanding. We conclude with design recommendations to improve fairness, scale invariance, and semantic grounding in future reference-free evaluation metrics.

A Diagnostic Framework for Auditing Reference-Free Vision-Language Metrics

There has been a growing research interest in in-image machine translation, which involves translating texts in images from one language to another. Recent studies continue to explore pipeline-based systems due to its straightforward construction and the consistent improvement of its underlined components. However, the existing implementation for such pipeline often lack extensibility, composability, and support for real-time translation. Therefore, this work introduces {\it \imagetra}---an open-source toolkit designed to facilitate the development of the pipeline-based system of in-image machine translation. The toolkit integrates state-of-the-art open-source models and tools, and is designed with a focus on modularity and efficiency, making it particularly well-suited for real-time translation. The toolkit is released at https://github.com/hour/imagetra.

ImageTra: Real-Time Translation for Texts in Image and Video

Live commentary has the potential of making specific broadcasts such as sports or video games more engaging and interesting to watch for spectators. With the recent popularity rise of online live streaming many new categories have entered the space, like art in its many forms or even software development, however, not all live streamers have the capability to be naturally engaging with the audience. We introduce a live commentator assistant system that can discuss what is visible on screen in real time. Our experimental setting is focused on the use-case of a photo editing live stream. We compare several recent vision language models for commentary generation and text to speech models for spoken output, all on relatively modest consumer hardware configurations.

Real-time Commentator Assistant for Photo Editing Live Streaming

We present LITMUS++, a prototype demo system that predicts model performance for queries of the form “How will a Model perform on a Task in a Language?” even when no benchmark exists. The system replaces static suites and opaque LLM-as-judge setups with an agentic, auditable workflow: a Directed Acyclic Graph (DAG) of specialized Thought Agents that hypothesize, retrieve multilingual evidence, select features, and train lightweight regressors with calibrated uncertainty. The web interface offers a chat entry point, a live reasoning view, and an evidence panel with citations and exportable reports. Experiments across six tasks and five multilingual scenarios show that LITMUS++ delivers accurate predictions with interpretable reasoning.

Downloads

Next from IJCNLP-AACL 2025

Synthetic Singers: A Review of Deep-Learning-based Singing Voice Synthesis Approaches

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES