Despite Southeast Asia’s (SEA) extraordinary linguistic and cultural diversity, the region remains significantly underrepresented in vision-language (VL) research, resulting in AI models that inadequately capture SEA cultural nuances. To fill this gap, we present SEA-VL, an open-source initiative dedicated to developing culturally relevant high-quality datasets for SEA languages. By involving contributors from SEA countries, SEA-VL ensures better cultural relevance and diversity, fostering greater inclusivity of underrepresented languages and cultural depictions in VL research. Our methodology employed three approaches: community-driven crowdsourcing with SEA contributors, automated image crawling, and synthetic image generation. We evaluated each method’s effectiveness in capturing cultural relevance. We found that image crawling achieves approximately ~85% cultural relevance while being more cost- and time-efficient than crowdsourcing, whereas synthetic image generation failed to accurately reflect SEA cultural nuances and contexts. Collectively, we gathered 1.28 million SEA culturally relevant images, more than 50 times larger than other existing datasets. This work bridges the representation gap in SEA, establishes a foundation for developing culturally aware AI systems for this region, and provides a replicable framework for addressing representation gaps in other underrepresented regions.

This paper presents DiReC (Disentangled Contrastive Representation), a novel two-stage framework designed to address the BEA 2025 Shared Task 5: Tutor Identity Classification. The task involves distinguishing between responses generated by nine different tutors, including both human educators and large language models (LLMs). DiReC leverages a disentangled representation learning approach, separating semantic content and stylistic features to improve tutor identification accuracy. In Stage 1, the model learns discriminative content representations using cross-entropy loss. In Stage 2, it applies supervised contrastive learning on style embeddings and introduces a disentanglement loss to enforce orthogonality between style and content spaces. Evaluated on the validation set, DiReC achieves strong performance, with a macro-F1 score of 0.9101 when combined with a CatBoost classifier and refined using the Hungarian algorithm. The system ranks third overall in the shared task with a macro-F1 score of 0.9172, demonstrating the effectiveness of disentangled representation learning for tutor identity classification.

This paper investigates approaches for the IWSLT low-resource track, Track 1 (speech-to-text translation) for the Maltese language, focusing on data augmentation and large pre-trained models. Our system combines Whisper for transcription and NLLB for translation, with experiments concentrated mainly on the translation stage. We observe that data augmentation leads to only marginal improvements, primarily for the smaller 600M model, with gains up to 0.0026 COMET points. These gains do not extend to larger models like the 3.3B NLLB, and the overall impact appears somewhat inconsistent. In contrast, fine-tuning larger models using QLoRA outperforms full fine-tuning of smaller models. Moreover, multi-stage fine-tuning consistently improves task-specific performance across all model sizes.