  We demonstrate that replacing an LSTM encoder with a self-attentive
architecture can lead to improvements to a state-of-the-art discriminative
constituency parser. The use of attention makes explicit the manner in which
information is propagated between different locations in the sentence, which we
use to both analyze our model and propose potential improvements. For example,
we find that separating positional and content information in the encoder can
lead to improved parsing accuracy. Additionally, we evaluate different
approaches for lexical representation. Our parser achieves new state-of-the-art
results for single models trained on the Penn Treebank: 93.55 F1 without the
use of any external data, and 95.13 F1 when using pre-trained word
representations. Our parser also outperforms the previous best-published
accuracy figures on 8 of the 9 languages in the SPMRL dataset.
