  Cross-lingual text classification alleviates the need for manually labeled
documents in a target language by leveraging labeled documents from other
languages. Existing approaches for transferring supervision across languages
require expensive cross-lingual resources, such as parallel corpora, while less
expensive cross-lingual representation learning approaches train classifiers
without target labeled documents. In this work, we propose a cross-lingual
teacher-student method, CLTS, that generates "weak" supervision in the target
language using minimal cross-lingual resources, in the form of a small number
of word translations. Given a limited translation budget, CLTS extracts and
transfers only the most important task-specific seed words across languages and
initializes a teacher classifier based on the translated seed words. Then, CLTS
iteratively trains a more powerful student that also exploits the context of
the seed words in unlabeled target documents and outperforms the teacher. CLTS
is simple and surprisingly effective in 18 diverse languages: by transferring
just 20 seed words, even a bag-of-words logistic regression student outperforms
state-of-the-art cross-lingual methods (e.g., based on multilingual BERT).
Moreover, CLTS can accommodate any type of student classifier: leveraging a
monolingual BERT student leads to further improvements and outperforms even
more expensive approaches by up to 12% in accuracy. Finally, CLTS addresses
emerging tasks in low-resource languages using just a small number of word
translations.
