Abstract
We consider the task of crosslingual semantic parsing in the style of Discourse Representation Theory (DRT) where knowledge from annotated corpora in a resource-rich language is transferred via bitext to guide learning in other languages. We introduce đť•Śniversal Discourse Representation Theory (đť•ŚDRT), a variant of DRT that explicitly anchors semantic representations to tokens in the linguistic input. We develop a semantic parsing framework based on the Transformer architecture and utilize it to obtain semantic resources in multiple languages following two learning schemes. The many-to-one approach translates non-English text to English, and then runs a relatively accurate English parser on the translated text, while the one-to-many approach translates gold standard English to non-English text and trains multiple parsers (one per language) on the translations. Experimental results on the Parallel Meaning Bank show that our proposal outperforms strong baselines by a wide margin and can be used to construct (silver-standard) meaning banks for 99 languages.- Anthology ID:
- 2021.cl-2.15
- Volume:
- Computational Linguistics, Volume 47, Issue 2 - June 2021
- Month:
- June
- Year:
- 2021
- Address:
- Cambridge, MA
- Venue:
- CL
- SIG:
- Publisher:
- MIT Press
- Note:
- Pages:
- 445–476
- Language:
- URL:
- https://aclanthology.org/2021.cl-2.15
- DOI:
- 10.1162/coli_a_00406
- Cite (ACL):
- Jiangming Liu, Shay B. Cohen, Mirella Lapata, and Johan Bos. 2021. Universal Discourse Representation Structure Parsing. Computational Linguistics, 47(2):445–476.
- Cite (Informal):
- Universal Discourse Representation Structure Parsing (Liu et al., CL 2021)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2021.cl-2.15.pdf
- Data
- Groningen Meaning Bank