Abstract
Previous work has predominantly focused on monolingual English semantic parsing. We, instead, explore the feasibility of Chinese semantic parsing in the absence of labeled data for Chinese meaning representations. We describe the pipeline of automatically collecting the linearized Chinese meaning representation data for sequential-to-sequential neural networks. We further propose a test suite designed explicitly for Chinese semantic parsing, which provides fine-grained evaluation for parsing performance, where we aim to study Chinese parsing difficulties. Our experimental results show that the difficulty of Chinese semantic parsing is mainly caused by adverbs. Realizing Chinese parsing through machine translation and an English parser yields slightly lower performance than training a model directly on Chinese data.- Anthology ID:
- 2023.naloma-1.7
- Volume:
- Proceedings of the 4th Natural Logic Meets Machine Learning Workshop
- Month:
- June
- Year:
- 2023
- Address:
- Nancy, France
- Editors:
- Stergios Chatzikyriakidis, Valeria de Paiva
- Venues:
- NALOMA | WS
- SIG:
- SIGSEM
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 62–74
- Language:
- URL:
- https://aclanthology.org/2023.naloma-1.7
- DOI:
- Cite (ACL):
- Chunliu Wang, Xiao Zhang, and Johan Bos. 2023. Discourse Representation Structure Parsing for Chinese. In Proceedings of the 4th Natural Logic Meets Machine Learning Workshop, pages 62–74, Nancy, France. Association for Computational Linguistics.
- Cite (Informal):
- Discourse Representation Structure Parsing for Chinese (Wang et al., NALOMA-WS 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2023.naloma-1.7.pdf