Document-Level Neural Machine Translation with Hierarchical Attention Networks
Table 3: Performance for variable context sizes k with the HAN encoder + HAN decoder.
['[ITALIC]  [BOLD] k', '[BOLD] TED Talks Zh–En', '[BOLD] TED Talks Es–En', '[BOLD] Subtitles Zh–En', '[BOLD] Subtitles Es–En', '[BOLD] News Es–En']
We can see that the best performance for TED talks and news is archived with 3, while for subtitles it is similar between 3 and 7.