DeepAI AI Chat
Log In Sign Up

Bootstrapping Multilingual Intent Models via Machine Translation for Dialog Automation

by   Nicholas Ruiz, et al.

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.


page 1

page 2

page 3

page 4


Quick Starting Dialog Systems with Paraphrase Generation

Acquiring training data to improve the robustness of dialog systems can ...

AllWOZ: Towards Multilingual Task-Oriented Dialog Systems for All

A commonly observed problem of the state-of-the-art natural language tec...

A Base Camp for Scaling AI

Modern statistical machine learning (SML) methods share a major limitati...

Why Do Neural Dialog Systems Generate Short and Meaningless Replies? A Comparison between Dialog and Translation

This paper addresses the question: Why do neural dialog systems generate...

Opportunities and Challenges in Neural Dialog Tutoring

Designing dialog tutors has been challenging as it involves modeling the...

HarperValleyBank: A Domain-Specific Spoken Dialog Corpus

We introduce HarperValleyBank, a free, public domain spoken dialog corpu...