Translate & Fill: Improving Zero-Shot Multilingual Semantic Parsing with Synthetic Data

Massimo Nicosia, Zhongdi Qu, Yasemin Altun


Abstract
While multilingual pretrained language models (LMs) fine-tuned on a single language have shown substantial cross-lingual task transfer capabilities, there is still a wide performance gap in semantic parsing tasks when target language supervision is available. In this paper, we propose a novel Translate-and-Fill (TaF) method to produce silver training data for a multilingual semantic parser. This method simplifies the popular Translate-Align-Project (TAP) pipeline and consists of a sequence-to-sequence filler model that constructs a full parse conditioned on an utterance and a view of the same parse. Our filler is trained on English data only but can accurately complete instances in other languages (i.e., translations of the English training utterances), in a zero-shot fashion. Experimental results on three multilingual semantic parsing datasets show that data augmentation with TaF reaches accuracies competitive with similar systems which rely on traditional alignment techniques.
Anthology ID:
2021.findings-emnlp.279
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2021
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Editors:
Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
Venue:
Findings
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
3272–3284
Language:
URL:
https://aclanthology.org/2021.findings-emnlp.279
DOI:
10.18653/v1/2021.findings-emnlp.279
Bibkey:
Cite (ACL):
Massimo Nicosia, Zhongdi Qu, and Yasemin Altun. 2021. Translate & Fill: Improving Zero-Shot Multilingual Semantic Parsing with Synthetic Data. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 3272–3284, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Translate & Fill: Improving Zero-Shot Multilingual Semantic Parsing with Synthetic Data (Nicosia et al., Findings 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.findings-emnlp.279.pdf
Video:
 https://aclanthology.org/2021.findings-emnlp.279.mp4
Data
MTOP