Supervised treebank conversion: Data and approaches

Treebank conversion is a straightforward and effective way to exploit various heterogeneous treebanks for boosting parsing accuracy. However, previous work mainly focuses on unsupervised treebank conversion and makes little progress due to the lack of manually labeled data where each sentence has two syntactic trees complying with two different guidelines at the same time, referred as bi-tree aligned data. In this work, we for the first time propose the task of supervised treebank conversion. First, we manually construct a bi-tree aligned dataset containing over ten thousand sentences. Then, we propose two simple yet effective treebank conversion approaches (pattern embedding and treeLSTM) based on the state-of-the-art deep biaffine parser. Experimental results show that 1) the two approaches achieve comparable conversion accuracy, and 2) treebank conversion is superior to the widely used multi-task learning framework in multiple treebank exploitation and leads to significantly higher parsing accuracy.