MSVD-Turkish: a comprehensive multimodal video dataset for integrated vision and language research in Turkish

Citamak B., Caglayan O., Kuyu M., Erdem E., Erdem A., Madhyastha P., ...More

MACHINE TRANSLATION, vol.35, pp.265-288, 2021 (Peer-Reviewed Journal) identifier identifier

  • Publication Type: Article / Article
  • Volume: 35
  • Publication Date: 2021
  • Doi Number: 10.1007/s10590-021-09276-y
  • Journal Indexes: Emerging Sources Citation Index, Scopus, FRANCIS, IBZ Online, Periodicals Index Online, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, Educational research abstracts (ERA), INSPEC, Library, Information Science & Technology Abstracts (LISTA), Linguistic Bibliography, Linguistics & Language Behavior Abstracts, MLA - Modern Language Association Database
  • Page Numbers: pp.265-288


Automatic generation of video descriptions in natural language, also called video captioning, aims to understand the visual content of the video and produce a natural language sentence depicting the objects and actions in the scene. This challenging integrated vision and language problem, however, has been predominantly addressed for English. The lack of data and the linguistic properties of other languages limit the success of existing approaches for such languages. In this paper we target Turkish, a morphologically rich and agglutinative language that has very different properties compared to English. To do so, we create the first large-scale video captioning dataset for this language by carefully translating the English descriptions of the videos in the MSVD (Microsoft Research Video Description Corpus) dataset into Turkish. In addition to enabling research in video captioning in Turkish, the parallel English-Turkish descriptions also enable the study of the role of video context in (multimodal) machine translation. In our experiments, we build models for both video captioning and multimodal machine translation and investigate the effect of different word segmentation approaches and different neural architectures to better address the properties of Turkish. We hope that the MSVD-Turkish dataset and the results reported in this work will lead to better video captioning and multimodal machine translation models for Turkish and other morphology rich and agglutinative languages.