Year: 2025
Type: conference-paper
Abstract: Multilingual alignment of sentence representations has mostly required bitexts to bridge the gap between languages.We investigate whether visual information can bridge this gap instead.Image caption datasets are very easy to create without requiring multilingual expertise, so this offers a more efficient alternative for low-resource languages.We find that multilingual image-caption alignment can implicitly align the text representations between languages, languages unseen by the encoder in pretraining can be incorporated into this alignment post-hoc, and these aligned representations are usable for cross-lingual Natural Language Understanding (NLU) and bitext retrieval.1
Institution: George Mason University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4412889472