The Efficacy of Human Post-Editing for Language Translation(2)

INTRODUCTION
High quality language translation is expensive. For exam- ple, the entire CHI proceedings from 1982 to 2011 contain 2,930 papers. Assuming roughly 5,000 words per paper and $0.15 per word—a representative translation rate for technical documents—the cost to translate the proceedings from English to just one other language is $2.2 million. Imagine: this sum is for one conference in one subfield of one academic discipline. To lower this cost, various forms of machine assistance have been devised: source (input) aids like bilingual dictionaries; target (output) aids such as spelling and grammar checkers; and post-editing (see [2]), the manual correction of fully auto- matic machine translation (MT) output. Language translation in practice is thus fundamentally an HCI task, with humans and machines working in concert.

Figure 1: Translation as post-editing. (a) Mouse hover events over the source sentence. The color and area of the circles indicate part of speech and mouse hover frequency, respectively, during translation to French. Nouns (blue) seem to be significant. (b) The user corrects two spans in the MT output to produce a final translation.