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Discriminative language models using n-gram features have been shown to be effective in reducing speech recognition word error rates. In this paper we describe a method for incorporating discourse-level triggers into a discriminative language model. Triggers are features identifying re-occurrence of words within a conversation. We introduce triggers that are specific to particular unigrams and bigrams, as well as "back off" trigger features that allow generalizations to be made across different unigrams. We train our model using a new loss-sensitive variant of the perceptron algorithm that makes effective use of information from multiple hypotheses in an n-best list. We train and test on the switchboard data set and show a 0.5 absolute reduction in WER over a baseline discriminative model which uses n-gram features alone, and a 1.5 absolute reduction in WER over the baseline recognizer.