Constituency Parsing with a Self-Attentive Encoder
Table 4: Development-set F1 scores for different approaches to handling morphology, with and without the addition of learned word embeddings.
['[EMPTY]', 'Word embeddings ✓', 'Word embeddings ✗']
If tag embeddings are removed from our model and only word embeddings remain (where word embeddings are learned jointly with other model parameters), performance suffers by around 1 F1. To restore performance without introducing any dependencies on an external system, we explore incorporating lexical features directly into our model.