Using crowdsourcing system for creating site-specific statistical machine translation engine

TL;DR

This paper explores using crowdsourcing to gather domain-specific linguistic data for training tailored statistical machine translation engines, enhancing translation accuracy for specific sites.

Contribution

It introduces a method for collecting site-specific parallel corpora via crowdsourcing to improve domain-adapted machine translation models.

Findings

Crowdsourcing effectively gathers domain-specific translation data.

Site-specific corpora improve translation quality for targeted content.

The approach enables scalable customization of machine translation systems.

Abstract

A crowdsourcing translation approach is an effective tool for globalization of site content, but it is also an important source of parallel linguistic data. For the given site, processed with a crowdsourcing system, a sentence-aligned corpus can be fetched, which covers a very narrow domain of terminology and language patterns - a site-specific domain. These data can be used for training and estimation of site-specific statistical machine translation engine