TL;DR
This paper introduces Semantic Parser Localizer (SPL), a toolkit that rapidly localizes semantic parsers for new languages using neural machine translation, achieving high accuracy in under a day.
Contribution
The paper presents a novel method combining machine translation and few-shot learning to localize semantic parsers for multiple languages efficiently.
Findings
Achieved 61-78% accuracy across new languages and domains.
Outperformed previous state-of-the-art by over 30-40%.
Enabled language localization in less than 24 hours.
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 models achieve an overall test accuracy ranging between 61% and 69% for the hotels domain and between 64% and 78% for restaurants domain, which compares…
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