Neural Simultaneous Speech Translation Using Alignment-Based Chunking
Table 2: Experimental results (in %) for simultaneous NMT of text input, IWSLT 2020 English→German.
['[BOLD] System', '[BOLD] Delay', '[BOLD] Avg.  [BOLD] AL', '[BOLD] tst2015  [BOLD] Bleu', '[BOLD] tst2015  [BOLD] Ter', '[BOLD] must-c-HE  [BOLD] Bleu', '[BOLD] must-c-HE  [BOLD] Ter', '[BOLD] must-c-COMMON  [BOLD] Bleu', '[BOLD] must-c-COMMON  [BOLD] Ter']
For this, we chunk the training data into single words, except for a first chunk of size k=9 on the source side, and set the delay to D=0. All of our systems outperform this wait-k system by large margins. Also for the text condition, we do not observe large differences between uni- and bidirectional models and between different delays. generated using a delay D of 2, 3, and 4.