  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
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.
