We present a detailed analysis of a graph- based annotation strategy that we employed to annotate a corpus of 11,292 real-world En- glish to Spanish automatic translations with relative (ranking) and absolute (adequate/non- adequate) quality assessments. The proposed approach, inspired by previous work in In- teractive Evolutionary Computation and Inter- active Genetic Algorithms, results in a sim- pler and faster annotation process. We em- pirically compare the method against a tra- ditional, explicit ranking approach, and show that the graph-based strategy: 1) is consider- ably faster, and 2) produces consistently more reliable annotations

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