Distilling Knowledge for Search-based Structured Prediction
Table 2: The dependency parsing results. Significance test Nilsson and Nivre (2008) shows the improvement of our Distill (both) over Baseline is statistically significant with p<0.01.
['[EMPTY]', 'LAS']
From this result, we can see that the ensemble model outperforms the baseline model by 1.90 in LAS. For our distillation from reference, when setting α=1.0, best performance on development set is achieved and the test LAS is 91.99. The second group shows the greedy transition-based parsers in previous literatures. Andor et al. Ballesteros et al. Our distillation parser outperforms all these greedy counterparts. The third group shows parsers trained on different techniques including decoding with beam search Buckman et al. ; Andor et al. Our distillation parser still outperforms its transition-based counterparts but lags the others. We attribute the gap between our parser with the other parsers to the difference in parsing algorithms.