Chris and Kyle discuss the importance of automatic evaluation measures, such as Blue, in machine translation systems. They explore the correlation between Blue scores and human judgments, highlighting the need for evidence to support claims of improved translation quality. Additionally, they uncover biases in Blue towards certain types of models, shedding light on the limitations of automatic evaluation.
