@inproceedings{moradshahi-etal-2020-localizing,
title = "Localizing Open-Ontology {QA} Semantic Parsers in a Day Using Machine Translation",
author = "Moradshahi, Mehrad and
Campagna, Giovanni and
Semnani, Sina and
Xu, Silei and
Lam, Monica",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.481",
doi = "10.18653/v1/2020.emnlp-main.481",
pages = "5970--5983",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Localizing Open-Ontology QA Semantic Parsers in a Day Using Machine Translation
%A Moradshahi, Mehrad
%A Campagna, Giovanni
%A Semnani, Sina
%A Xu, Silei
%A Lam, Monica
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F moradshahi-etal-2020-localizing
%X 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.
%R 10.18653/v1/2020.emnlp-main.481
%U https://aclanthology.org/2020.emnlp-main.481
%U https://doi.org/10.18653/v1/2020.emnlp-main.481
%P 5970-5983
Markdown (Informal)
[Localizing Open-Ontology QA Semantic Parsers in a Day Using Machine Translation](https://aclanthology.org/2020.emnlp-main.481) (Moradshahi et al., EMNLP 2020)
ACL