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This paper describes Meteor-WSD and RATATOUILLE, the LIMSI submissions to the WMT15 metrics shared task. MeteorWSD extends synonym mapping to languages other than English based on alignments and gives credit to semantically adequate translations in context. We show that context-sensitive synonym selection increases the correlation of the Meteor metric with human judgments of translation quality on the WMT14 data. RATATOUILLE combines MeteorWSD with nine other metrics for evaluation and outperforms the best metric (BEER) involved in its computation.

Alignment-based sense selection in METEOR and the RATATOUILLE recipe