We introduce two first-order graph-based dependency parsers achieving a new
state of the art. The first is a consensus parser built from an ensemble of
independently trained greedy LSTM transition-based parsers with different
random initializations. We cast this approach as minimum Bayes risk decoding
(under the Hamming cost) and argue that weaker consensus within the ensemble is
a useful signal of difficulty or ambiguity. The second parser is a
"distillation" of the ensemble into a single model. We train the distillation
parser using a structured hinge loss objective with a novel cost that
incorporates ensemble uncertainty estimates for each possible attachment,
thereby avoiding the intractable cross-entropy computations required by
applying standard distillation objectives to problems with structured outputs.
The first-order distillation parser matches or surpasses the state of the art
on English, Chinese, and German.

介绍了两个第一阶段基于图的依存解析器，第一个是通过集成学习同质模型的共识解析器，利用最小贝叶斯风险解码法和 Hamming 代价，结合集成中的不确定性评估进行训练，第二个则是将集成模型压缩成单一模型的 'distillation' 解析器，利用新颖的结构化铰链损失函数避免了普通 'distillation' 目标的不可解交叉熵，首次在英语、汉语和德语上达到或超过了现有最先进水平。