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
- Venue:
- IWPT
- SIG:
- SIGPARSE
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 44–49
- Language:
- URL:
- https://aclanthology.org/W17-6306
- DOI:
- 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., IWPT 2017)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/W17-6306.pdf
- Data
- Universal Dependencies