Localizing Open-Ontology QA Semantic Parsers in a Day Using Machine Translation

Mehrad Moradshahi, Giovanni Campagna, Sina Semnani, Silei Xu, Monica Lam


Abstract
We propose Semantic Parser Localizer (SPL), a toolkit that leverages Neural Machine Translation (NMT) systems to localize a semantic parser for a new language. Our methodology is to (1) generate training data automatically in the target language by augmenting machine-translated datasets with local entities scraped from public websites, (2) add a few-shot boost of human-translated sentences and train a novel XLMR-LSTM semantic parser, and (3) test the model on natural utterances curated using human translators. We assess the effectiveness of our approach by extending the current capabilities of Schema2QA, a system for English Question Answering (QA) on the open web, to 10 new languages for the restaurants and hotels domains. Our model achieves an overall test accuracy ranging between 61% and 69% for the hotels domain and between 64% and 78% for restaurants domain, which compares favorably to 69% and 80% obtained for English parser trained on gold English data and a few examples from validation set. We show our approach outperforms the previous state-of-the-art methodology by more than 30% for hotels and 40% for restaurants with localized ontologies for the subset of languages tested. Our methodology enables any software developer to add a new language capability to a QA system for a new domain, leveraging machine translation, in less than 24 hours. Our code is released open-source.
Anthology ID:
2020.emnlp-main.481
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5970–5983
Language:
URL:
https://aclanthology.org/2020.emnlp-main.481
DOI:
10.18653/v1/2020.emnlp-main.481
Bibkey:
Cite (ACL):
Mehrad Moradshahi, Giovanni Campagna, Sina Semnani, Silei Xu, and Monica Lam. 2020. Localizing Open-Ontology QA Semantic Parsers in a Day Using Machine Translation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 5970–5983, Online. Association for Computational Linguistics.
Cite (Informal):
Localizing Open-Ontology QA Semantic Parsers in a Day Using Machine Translation (Moradshahi et al., EMNLP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.481.pdf
Video:
 https://slideslive.com/38939374
Code
 stanford-oval/SPL