Relative Positional Encoding for Speech Recognition and Direct Translation
Table 4: ST: Translation performance in BLEU↑ on IWSLT testsets (re-segmentation required)
['[ITALIC] Testset →  [ITALIC] Segmenter →', '[BOLD] Transformer LIUM', '[BOLD] Transformer VAD', '[BOLD] Relative LIUM', '[BOLD] Relative VAD', '[BOLD] Cascade LIUM', '[BOLD] Cascade VAD']
The relative Transformer unsurprisingly consistently outperforms the Transformer, regardless of segmentation. Moreover, comparing between the segmenters, the relative model more effectively uses higher segmentation quality, yielding a larger BLEU difference. While the base Transformer only increases up to 0.5 BLEU with better segmentation, this figure becomes up to 2.4 BLEU points for the relative counterpart. In the end, the cascade model still shows that heavily tuned separated components, together with an explicit text segmentation module, is an advantage over end-to-end models, but this gap is closing with more efficient architectures.