Is 42 the Answer to Everything in Subtitling-oriented Speech Translation?
Table 1: Results for translation quality (BLEU, BLEU-nob), for conformity to the subtitling constraints (CPL, CPS) and for subtitle segmentation (TER-br) for the four systems. Results marked with † are not statistically significant.
['FR', 'Template', 'BLEU↑ 30.62', 'BLEU-nob↑ 28.86', 'CPL↑ 91%', 'CPS↑ 68%', 'TER-br↓ 18']
E2E exceeds all models at achieving proper length of subtitles, with 95% and 93% of the subtitles having length of maximum 42 characters. E2E achieves higher conformity with length even compared to the Template, for which the segmentation is already provided to the system in the form of break symbols, while the cascade is behind by 2%. The same tendency is observed in the TER-br results computed to measure the proper placement of the break symbols. While the Template benefits from the source language segmentation and therefore requires less edits to properly segment the subtitles, the Cascade is disadvantaged in guessing the correct position of the break symbols, as shown by a 22 and 21 TER score. For E2E-small TER-br is higher, possibly due to the low translation quality. However, E2E outperforms the Cascade by 1 TER point in this respect, showing that less effort would be required to segment the sentences into subtitles. This suggests that the E2E system receives information compared to the Cascade, which allows for better guessing the positions of the break symbols in the translation. This is another indication that subtitle segmentation decisions are not solely determined by reaching a maximum length of 42 characters, but a combination of multiple (possibly intersemiotic) factors can offer a better answer to automatic subtitling.