Attentive Tensor Product Learning
Table 2: Performance of POS Tagger.
['[EMPTY]', ' WSJ 22', ' WSJ 23', 'Our POS tagger WSJ 22', 'Our POS tagger WSJ 23']
Once the model gets trained, we test it on WSJ Section 22 and 23 respectively. From the table, we can observe that our approach outperforms the baseline. This confirms our hypothesis that the unsupervisely trained unbinding vector ut indeed captures grammatical information, so as to be used to effectively predict grammar structures such as POS tags.