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.
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.
@cite_21
2115550799 2251654371 2291319738 1569447338
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. This paper presents the AMARA corpus of on-line educational content: a new parallel corpus of educational video subtitles, multilingually aligned for 20 languages, i.e. 20 monolingual corpora and 190 parallel corpora. This corpus includes both resource-rich languages such as English and Arabic, and resource-poor languages such as Hindi and Thai. In this paper, we describe the gathering, validation, and preprocessing of a large collection of parallel, community-generated subtitles. Furthermore, we describe the methodology used to prepare the data for Machine Translation tasks. Additionally, we provide a document-level, jointly aligned development and test sets for 14 language pairs, designed for tuning and testing Machine Translation systems. We provide baseline results for these tasks, and highlight some of the challenges we face when building machine translation systems for educational content. FP7-ICT Collaborative Project ICT-2013-611233 PHEME Deliverable D2.1 (WP2) This document outlines a preliminary definition of an annotation scheme for rumours spread through social media, as well as the code frames that will be used to mark up the corpora collected for PHEME. It has been developed through an iterative process of revisions, and it is intended to encompass the different kinds of rumours that can be spread and discussed in the context of different events and situations. It especially considers the way conversations flow in social media, and has been developed in an interdisciplinary style by building on work in sociolinguistics, including the related approaches of Conversation Analysis [51] and Ethnomethodology [16]. The resulting annotation scheme will be used for the annotation of a larger corpora of social media rumours through a crowdsourcing platform. Annotation guidelines will be defined in following work and tested with small social media corpora before running the large annotation task. Keyword list: rumors, veracity, annotation scheme, social media To support multi-disciplinary research in the AMI (Augmented Multi-party Interaction) project, a 100 hour corpus of meetings is being collected. This corpus is being recorded in several instrumented rooms equipped with a variety of microphones, video cameras, electronic pens, presentation slide capture and white-board capture devices. As well as real meetings, the corpus contains a significant proportion of scenario-driven meetings, which have been designed to elicit a rich range of realistic behaviors. To facilitate research, the raw data are being annotated at a number of levels including speech transcriptions, dialogue acts and summaries. The corpus is being distributed using a web server designed to allow convenient browsing and download of multimedia content and associated annotations. This article first overviews AMI research themes, then discusses corpus design, as well as data collection, annotation and distribution.