Multi-channel Reverse Dictionary Model
Table 5: Reverse dictionary performance with prior knowledge.
['Prior Knowledge None', '[BOLD] Seen Definition 1', '[BOLD] Seen Definition .49/.78/.90', '[BOLD] Seen Definition 220', '[BOLD] Useen Definition 10', '[BOLD] Useen Definition .18/.49/.76', '[BOLD] Useen Definition 310', '[BOLD] Description 5', '[BOLD] Description .24/.56/.82', '[BOLD] Description 260', '[BOLD] Question 0', '[BOLD] Question .50/.73/.90', '[BOLD] Question 223']
We can also find that any prior knowledge can improve our model’s performance, especially the initial character information. That is presumably because the average character number of Chinese words is much less than that of English words and the search space is reduced to be smaller. Similar to English, the performance improvement of our model given POS tag information is also insignificant, which also demonstrates that our model can do well in POS tag prediction.