  Natural language processing is heavily Anglo-centric, while the demand for
models that work in languages other than English is greater than ever. Yet, the
task of transferring a model from one language to another can be expensive in
terms of annotation costs, engineering time and effort. In this paper, we
present a general framework for easily and effectively transferring neural
models from English to other languages. The framework, which relies on task
representations as a form of weak supervision, is model and task agnostic,
meaning that many existing neural architectures can be ported to other
languages with minimal effort. The only requirement is unlabeled parallel data,
and a loss defined over task representations. We evaluate our framework by
transferring an English sentiment classifier to three different languages. On a
battery of tests, we show that our models outperform a number of strong
baselines and rival state-of-the-art results, which rely on more complex
approaches and significantly more resources and data. Additionally, we find
that the framework proposed in this paper is able to capture semantically rich
and meaningful representations across languages, despite the lack of direct
supervision.
