@inproceedings{ponwitayarat-etal-2026-sea,
    title = "{SEA}-{BED}: How Do Embedding Models Represent {S}outheast {A}sian Languages?",
    author = "Ponwitayarat, Wuttikorn  and
      Limkonchotiwat, Peerat  and
      Ng, Raymond  and
      Montalan, Jann Railey  and
      Aung, Thura  and
      Ngui, Jian Gang  and
      Susanto, Yosephine  and
      Tjhi, William Chandra  and
      Tasawong, Panuthep  and
      Cambria, Erik  and
      Chuangsuwanich, Ekapol  and
      Nutanong, Sarana",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.397/",
    pages = "8788--8822",
    ISBN = "979-8-89176-390-6",
    abstract = "Multilingual text embeddings are often assumed to encode meaning in a perspective-independent semantic space, yielding stable similarity judgments across tasks and languages. Our results show that this assumption does not hold in practice. We introduce SEA-BED, a large-scale benchmark covering 10 Southeast Asian (SEA) languages and diverse embedding tasks, designed to systematically examine how embedding performance varies across tasks, languages, and language-task combinations. Across extensive evaluations, we observe that no single model performs uniformly well across SEA languages; task difficulty differs markedly within languages, and success on one task does not reliably generalize to others. Language-task analyses further reveal highly non-uniform performance landscapes, where performance varies across different language-task combinations. These findings call for closer attention to performance measurements that provide an expansive view across languages and tasks to uncover inconsistencies in semantic representation. Based on these observations, we provide insights for future model development, including data, algorithmic, and architectural considerations."
}