Bootstrapping Multilingual Intent Models via Machine Translation for Dialog Automation

TL;DR

This paper investigates using machine translation to quickly develop multilingual dialog systems and assist human analysts in understanding foreign language utterances, demonstrating promising results in English-Spanish scenarios.

Contribution

It introduces a user study evaluating machine translation for bootstrapping multilingual dialog models and aiding human analysts, highlighting its effectiveness in dialog automation.

Findings

High potential for dialog automation with machine translation

Human analysts can accurately process foreign language utterances

Effective in English-Spanish language pair

Abstract

With the resurgence of chat-based dialog systems in consumer and enterprise applications, there has been much success in developing data-driven and rule-based natural language models to understand human intent. Since these models require large amounts of data and in-domain knowledge, expanding an equivalent service into new markets is disrupted by language barriers that inhibit dialog automation. This paper presents a user study to evaluate the utility of out-of-the-box machine translation technology to (1) rapidly bootstrap multilingual spoken dialog systems and (2) enable existing human analysts to understand foreign language utterances. We additionally evaluate the utility of machine translation in human assisted environments, where a portion of the traffic is processed by analysts. In English->Spanish experiments, we observe a high potential for dialog automation, as well as the potential for human analysts to process foreign language utterances with high accuracy.