Incremental Parsing with Minimal Features Using Bi-Directional LSTM
Table 3: Ablation studies on PTB dev set (wsj 22). Forward and backward context, and part-of-speech input were all critical to strong performace.
['Parser', 'UAS', 'LAS']
We found that performance could be improved, however, by factoring out the decision over structural actions (i.e., shift, left-reduce, or right-reduce) and the decision of which arc label to assign upon a reduce. We therefore use separate classifiers for those decisions, each with its own fully-connected hidden and output layers but sharing the underlying recurrent architecture. Using only word forms and no part-of-speech input similarly degraded performance.