TrustRank: Inducing Trust in Automatic Translations via Ranking

The adoption of Machine Translation technology for commercial applications is hampered by the lack of trust associated with machine-translated output. In this paper, we describe TrustRank, an MT system enhanced with a capability to rank the quality of translation outputs from good to bad. This enables the user to set a quality threshold, granting the user control over the quality of the translations. We quantify the gains we obtain in translation quality, and show that our solution works on a wide variety of domains and language pairs.

• Type: Journal
• Year: 2010
• Where: ACL
• Authors: Radu Soricut, Abdessamad Echihabi
• Keywords: ACL 2010, Computational Linguistics, Machine Translation Technology, Machine-translated Output, Translation Outputs