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.
For alignment, Tiedemann, started with the traditional approach of length-based sentence alignment @cite_16 using sentence boundaries tagged in an earlier stage. This is based on the idea that sentences are linguistically-driven elements and, thus, it would be more appropriate to process them using linguistic features rather than merely aligning subtitles appearing simultaneously on the screen. Nonetheless, there are many untranslated parts of the movie for reasons discussed earlier. The initial results were unsatisfactory.
@cite_16
1533246198 118129934 1779819959 1537949628
Sentence alignment is an essential step in building a parallel corpus. In this paper a specialized approach for the alignment of movie subtitles based on time overlaps is introduced. It is used for creating an extensive multilingual parallel subtitle corpus currently containing about 21 million aligned sentence fragments in 29 languages. Our alignment approach yields significantly higher accuracies compared to standard length-based approaches on this data. Furthermore, we can show that simple heuristics for subtitle synchronization can be used to improve the alignment accuracy even further. This paper presents a method for compiling a large-scale bilingual corpus from a database of movie subtitles. To create the corpus, we propose an algorithm based on Gale and Church’s sentence alignment algorithm(1993). However, our algorithm not only relies on character length information, but also uses subtitle-timing information, which is encoded in the subtitle files. Timing is highly correlated between subtitles in different versions (for the same movie), since subtitles that match should be displayed at the same time. However, the absolute time values can’t be used for alignment, since the timing is usually specified by frame numbers and not by real time, and converting it to real time values is not always possible, hence we use normalized subtitle duration instead. This results in a significant reduction in the alignment error rate. In this paper on-going work of creating an extensive multilingual parallel corpus of movie subtitles is presented. The corpus currently contains roughly 23,000 pairs of aligned subtitles covering about 2,700 movies in 29 languages. Subtitles mainly consist of transcribed speech, sometimes in a very condensed way. Insertions, deletions and paraphrases are very frequent which makes them a challenging data set to work with especially when applying automatic sentence alignment. Standard alignment approaches rely on translation consistency either in terms of length or term translations or a combination of both. In the paper, we show that these approaches are not applicable for subtitles and we propose a new alignment approach based on time overlaps specifically designed for subtitles. In our experiments we obtain a significant improvement of alignment accuracy compared to standard length-based This paper addresses the problem of synchronizing movie subtitles, which is necessary to improve alignment quality when building a parallel corpus out of translated subtitles. In particular, synchronization is done on the basis of aligned anchor points. Previous studies have shown that cognate filters are useful for the identification of such points. However, this restricts the approach to related languages with similar alphabets. Here, we propose a dictionary-based approach using automatic word alignment. We can show an improvement in alignment quality even for related languages compared to the cognate-based approach.