Detecting Foodborne Illness Complaints in Multiple Languages Using E nglish Annotations Only (2020)

Abstract: 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.

## introduction with the rise of social media, more and more users post online documents where they disclose serious ## background in this section, we provide background on foodborne illness detection (section 2.1) and crosslingual text classification (section 2.2). ## foodborne illness detection in multiple languages we now define our problem of focus (section 3.1) and describe our cross-lingual learning approach (sections 3.2 and 3.3). ## experiments we evaluate our approach on foodborne detection in english (en), spanish (es), chinese (zh), french (fr), german (de), japanese (ja), and italian (it). ## discussion and future work we presented our cross-lingual learning method for scaling foodborne illness detection to languages beyond english without extra annotations for non-english languages. as most reviews do not discuss foodborne illness, it is challenging to create proper evaluation datasets for all languages. in our preliminary experiments, we evaluated our approach on non-english languages by translating labeled test reviews from english to other languages. a caveat of this evaluation approach is that complaints of foodborne illness in nativelanguage reviews may be expressed differently than