L1-L2 Parallel Dependency Treebank as Learner Corpus

This opinion paper proposes the use of parallel treebank as learner corpus. We show how an L1-L2 parallel treebank - i.e., parse trees of non-native sentences, aligned to the parse trees of their target hypotheses - can facilitate retrieval of sentences with specific learner errors. We argue for its benefits, in terms of corpus reuse and interoperability, over a conventional learner corpus annotated with error tags. As a proof of concept, we conduct a case study on word-order errors made by learners of Chinese as a foreign language. We report precision and recall in retrieving a range of word-order error categories from L1-L2 tree pairs annotated in the Universal Dependency framework. © 2017 Association for Computational Linguistics