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With the resurgence of chat-based dialog systems in consumer and enterpriseapplications, there has been much success in developing data-driven andrule-based natural language models to understand human intent. Since thesemodels require large amounts of data and in-domain knowledge, expanding anequivalent service into new markets is disrupted by language barriers thatinhibit dialog automation.  This paper presents a user study to evaluate the utility of out-of-the-boxmachine translation technology to (1) rapidly bootstrap multilingual spokendialog systems and (2) enable existing human analysts to understand foreignlanguage utterances. We additionally evaluate the utility of machinetranslation in human assisted environments, where a portion of the traffic isprocessed by analysts. In English->Spanish experiments, we observe a highpotential for dialog automation, as well as the potential for human analysts toprocess foreign language utterances with high accuracy.

Bootstrapping Multilingual Intent Models via Machine Translation for Dialog Automation