  Health departments have been deploying text classification systems for the
early detection of foodborne illness complaints in social media documents such
as Yelp restaurant reviews. Current systems have been successfully applied for
documents in English and, as a result, a promising direction is to increase
coverage and recall by considering documents in additional languages, such as
Spanish or Chinese. Training previous systems for more languages, however,
would be expensive, as it would require the manual annotation of many documents
for each new target language. To address this challenge, we consider
cross-lingual learning and train multilingual classifiers using only the
annotations for English-language reviews. Recent zero-shot approaches based on
pre-trained multi-lingual BERT (mBERT) have been shown to effectively align
languages for aspects such as sentiment. Interestingly, we show that those
approaches are less effective for capturing the nuances of foodborne illness,
our public health application of interest. To improve performance without extra
annotations, we create artificial training documents in the target language
through machine translation and train mBERT jointly for the source (English)
and target language. Furthermore, we show that translating labeled documents to
multiple languages leads to additional performance improvements for some target
languages. We demonstrate the benefits of our approach through extensive
experiments with Yelp restaurant reviews in seven languages. Our classifiers
identify foodborne illness complaints in multilingual reviews from the Yelp
Challenge dataset, which highlights the potential of our general approach for
deployment in health departments.
