L1-L2 Parallel Dependency Treebank as Learner Corpus

John Lee, Keying Li, Herman Leung


Abstract
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 re-use 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.
Anthology ID:
W17-6306
Volume:
Proceedings of the 15th International Conference on Parsing Technologies
Month:
September
Year:
2017
Address:
Pisa, Italy
Venues:
IWPT | WS
SIG:
SIGPARSE
Publisher:
Association for Computational Linguistics
Note:
Pages:
44–49
Language:
URL:
https://aclanthology.org/W17-6306
DOI:
Bibkey:
Cite (ACL):
John Lee, Keying Li, and Herman Leung. 2017. L1-L2 Parallel Dependency Treebank as Learner Corpus. In Proceedings of the 15th International Conference on Parsing Technologies, pages 44–49, Pisa, Italy. Association for Computational Linguistics.
Cite (Informal):
L1-L2 Parallel Dependency Treebank as Learner Corpus (Lee et al., 2017)
Copy Citation:
PDF:
https://aclanthology.org/W17-6306.pdf
Data
Universal Dependencies