Are Subtitling Corpora really Subtitle-like?
Authors: Alina Karakanta, Matteo Negri, Marco Turchi (Fondazione Bruno Kessler, Università di Trento, 2019)
Topic: Machine Translation

Abstract
Growing needs in translating multimedia content have resulted in Neural Machine Translation (NMT) gradually becoming an established practice in the field of subtitling. Contrary to text translation, subtitling is subject to spatial and temporal constraints, which greatly increase the postprocessing effort required to restore the NMT output to a proper subtitle format. In this work, we explore whether existing subtitling corpora conform to the constraints of: 1) length and reading speed; and 2) proper line breaks. We show that the process of creating parallel sentence alignments removes important time and line break information and propose practices for creating resources for subtitlingoriented NMT faithful to the subtitle format.

Introduction
Machine Translation (MT) of subtitles is a growing need for various applications, given the amounts of online multimedia content becoming available daily. Subtitling translation is a complex process consisting of several stages (transcription, translation, timing), and manual approaches to the task are laborious and costly. Subtitling has to conform to spatial constraints such as length, and temporal constraints such as reading speed. While length and reading speed can be modelled as a post-processing step in an MT workflow using simple rules, subtitle segmentation, i.e. where and if to insert a line break, depends on semantic and syntactic properties. Subtitle segmentation is particularly important, since it has been shown that a proper segmentation by phrase or sentence significantly reduces reading time and improves comprehension (AUTHOR; AUTHOR). Hence, there is ample room for developing fully or at least partially automated solutions for subtitle-oriented NMT, which would contribute in reducing post-processing effort and speedingup turn-around times. Automated approaches though, especially NMT, are data-hungry. Performance greatly depends on the availability of large amounts of high-quality data (up to tens of millions of parallel sentences), specifically tailored for the task. In the case of subtitle-oriented NMT, this implies having access to large subtitle training corpora. This leads to the following question: What should data specifically tailored for subtitling-oriented NMT look like? There are large amounts of available parallel data extracted from subtitles (AUTHOR; AUTHOR; AUTHOR). These corpora are usually obtained by collecting files in a subtitle specific format (.srt) in several languages and then parsing and aligning them at sentence level. MT training at sentence level generally increases performance as the system receives longer context (useful, for instance, to disambiguate words). As shown in Table 1, this process compromises the subtitle format by converting the subtitle blocks into full sentences. With this “merging”, information about subtitle segmentation (line breaks) is often lost. Therefore, recovery of the MT output to a proper subtitle format has to be performed subsequently, either as a post-editing process or by using hand-crafted rules and boundary predictions. Integrating the subtitle constraints in the model can help reduce the post-processing effort, especially in cases where the input is a stream of data, such as in endto-end Speech Neural Machine Translation. To date, there has been no study examining the consequences of obtaining parallel sentences from subtitles on preserving the subtitling constraints. In this work, we explore whether the large, publicly available parallel data compiled from subtitles conform to the temporal and spatial constraints necessary for achieving quality subtitles. We compare the existing resources to an adaptation of MuST-C AUTHOR, where the data is kept as subtitles. For evaluating length and reading speed, we employ character counts, while for proper line breaks we use the Chink-Chunk algorithm AUTHOR. Based on the analysis, we discuss limitations of the existing data and present a preliminary road-map towards creating resources for training subtitling-oriented NMT faithful to the subtitling format.

Conclusion
We explored whether the existing parallel subtitling resources conform to the subtitling constraints. We found that subtitling corpora generally conform to length and proper line breaks, despite the merging operations for aligning parallel sentences. We isolated some missing elements: the lack of time information (duration of utterance) and the insufficient representation of line breaks other than at punctuation marks. This raises several open issues for creating corpora for subtitling-oriented NMT; i) subtitling constraints: a subtitling corpus, in order to be representative of the task, should respect the subtitling constraints; ii) duration of utterance: since the translation of a subtitle depends on the duration of the utterance, time information is highly relevant; iii) integrity of documents: a subtitle often occupies several lines, therefore the order of subtitles should be preserved whenever possible; iv) line break information: while parallel sentence alignments are indispensable, they should not compromise line break and subtitle block information. Break information could be preserved by inserting special symbols. We intend to use these observations for an adaptation of MuST-C, containing triplets (audio, source language subtitle, target language subtitle), preserving line break information and taking advantage of natural pauses in the audio. In the long run, we would like to train NMT systems which predict line breaks while translating, possibly extending the input context using methods from document level translation.