We describe efforts towards getting better resources for English-Arabic machine translation of spoken text. In particular, we look at movie subtitles as a unique, rich resource, as subtitles in one language often get translated into other languages. Movie subtitles are not new as a resource and have been explored in previous research; however, here we create a much larger bi-text (the biggest to date), and we further generate better quality alignment for it. Given the subtitles for the same movie in different languages, a key problem is how to align them at the fragment level. Typically, this is done using length-based alignment, but for movie subtitles, there is also time information. Here we exploit this information to develop an original algorithm that outperforms the current best subtitle alignment tool, subalign. The evaluation results show that adding our bi-text to the IWSLT training bi-text yields an improvement of over two BLEU points absolute.
@cite_21: In this paper, we present the main features of the Amara project, and its impact on the accessibility landscape with the use of innovative technology. We also show the effectiveness of volunteer communities in addressing large subtitling and translation tasks, that accompany the ever-growing amounts of online video content. Furthermore, we present two different applications for the platform. First, we examine the growing interest of organizations to build their own subtitling communities. Second, we present how the community-generated material can be used to advance the state-of-the-art of research in fields such as Statistical Machine Translation with focus on educational translation. We provide examples on how both tasks can be achieved successfully.
It is also worth mentioning the AMARA project https: www.amara.org en @cite_21 , a unique, open, scalable and flexible online collaborative platforms which takes advantage of the power of crowdsourcing and encourages volunteer translation and editing of subtitles of educational videos.