Machine Translation Aided Bilingual Data-to-Text Generation and Semantic Parsing

Oshin Agarwal, Mihir Kale, Heming Ge, Siamak Shakeri, Rami Al-Rfou


Abstract
We present a system for bilingual Data-ToText Generation and Semantic Parsing. We use a text-to-text generator to learn a single model that works for both languages on each of the tasks. The model is aided by machine translation during both pre-training and fine-tuning. We evaluate the system on WebNLG 2020 data 1 , which consists of RDF triples in English and natural language sentences in English and Russian for both the tasks. We achieve considerable gains over monolingual models, especially on unseen relations and Russian.
Anthology ID:
2020.webnlg-1.13
Volume:
Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)
Month:
12
Year:
2020
Address:
Dublin, Ireland (Virtual)
Venues:
INLG | WebNLG
SIG:
SIGGEN
Publisher:
Association for Computational Linguistics
Note:
Pages:
125–130
Language:
URL:
https://aclanthology.org/2020.webnlg-1.13
DOI:
Bibkey:
Cite (ACL):
Oshin Agarwal, Mihir Kale, Heming Ge, Siamak Shakeri, and Rami Al-Rfou. 2020. Machine Translation Aided Bilingual Data-to-Text Generation and Semantic Parsing. In Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+), pages 125–130, Dublin, Ireland (Virtual). Association for Computational Linguistics.
Cite (Informal):
Machine Translation Aided Bilingual Data-to-Text Generation and Semantic Parsing (Agarwal et al., WebNLG 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.webnlg-1.13.pdf