Ambiguity-aware ensemble training for semi-supervised dependency parsing

This paper proposes a simple yet effective framework for semi-supervised dependency parsing at entire tree level, referred to as ambiguity-aware ensemble training. Instead of only using 1-best parse trees in previous work, our core idea is to utilize parse forest (ambiguous labelings) to combine multiple 1-best parse trees generated from diverse parsers on unlabeled data. With a conditional random field based probabilistic dependency parser, our training objective is to maximize mixed likelihood of labeled data and auto-parsed unlabeled data with ambiguous labelings. This framework offers two promising advantages. 1) ambiguity encoded in parse forests compromises noise in 1-best parse trees. During training, the parser is aware of these ambiguous structures, and has the flexibility to distribute probability mass to its preferred parse trees as long as the likelihood improves. 2) diverse syntactic structures produced by different parsers can be naturally compiled into forest, offering complementary strength to our single-view parser. Experimental results on benchmark data show that our method significantly outperforms the baseline supervised parser and other entire-tree based semi-supervised methods, such as self-training, co-training and tri-training.