English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

We present a large-scale video subtitle translation dataset, BigVideo, tofacilitate the study of multi-modality machine translation. Compared with thewidely used How2 and VaTeX datasets, BigVideo is more than 10 times larger,consisting of 4.5 million sentence pairs and 9,981 hours of videos. We alsointroduce two deliberately designed test sets to verify the necessity of visualinformation: Ambiguous with the presence of ambiguous words, and Unambiguous inwhich the text context is self-contained for translation. To better model thecommon semantics shared across texts and videos, we introduce a contrastivelearning method in the cross-modal encoder. Extensive experiments on theBigVideo show that: a) Visual information consistently improves the NMT modelin terms of BLEU, BLEURT, and COMET on both Ambiguous and Unambiguous testsets. b) Visual information helps disambiguation, compared to the strong textbaseline on terminology-targeted scores and human evaluation. Dataset and ourimplementations are available at https://github.com/DeepLearnXMU/BigVideo-VMT.

BigVideo: A Large-scale Video Subtitle Translation Dataset for Multimodal Machine Translation