Crowdsourcing is recently used to automate complex tasks when computational systems alone fail. In this project, we investigate how humans can effectively contribute to automate natural language translation. The envisioned goal is a hybrid machine translation service that incrementally adapts machine translation models to new domains by employing human computation to make machine translation more competitive. Therefore, we investigate efficient ways for domain adoption of neural machine translation systems using crowd-generated input data.

Poster at Collective Intelligence 2018

In this work, we investigated (1) whether a (paid) crowd, that is acquired from a multilingual website’s community, is capable of translating coherent content from English to their mother tongue; and (2) in which cases state-of-the-art machine translation models can compete with human translations for automation in order to reduce task completion times and costs.



Michael Barz and Marimuthu Kalimuthu

Supported by