Verb Tense Generation

Correct usage of verb tenses is important because they encode the temporal order of events in a text. However, tense systems vary from one language to another, and are difficult to master for machines and non-native speakers alike. We present a method to predict verb tenses based on syntactic and lexical features, as well as temporal expressions in the context. A statistical model trained on Conditional Random Fields significantly outperforms the baseline. This model may be used in post-editing verbs in machine translation output and texts written by non-native speakers.