  This paper presents a novel reranking model, future reward reranking, to
re-score the actions in a transition-based parser by using a global scorer.
Different to conventional reranking parsing, the model searches for the best
dependency tree in all feasible trees constraining by a sequence of actions to
get the future reward of the sequence. The scorer is based on a first-order
graph-based parser with bidirectional LSTM, which catches different parsing
view compared with the transition-based parser. Besides, since context
enhancement has shown substantial improvement in the arc-stand transition-based
parsing over the parsing accuracy, we implement context enhancement on an
arc-eager transition-base parser with stack LSTMs, the dynamic oracle and
dropout supporting and achieve further improvement. With the global scorer and
context enhancement, the results show that UAS of the parser increases as much
as 1.20% for English and 1.66% for Chinese, and LAS increases as much as 1.32%
for English and 1.63% for Chinese. Moreover, we get state-of-the-art LASs,
achieving 87.58% for Chinese and 93.37% for English.
