For example, by leveraging both topical content and discourse structure, our model achieves a mean average precision (MAP) of 0.76 on conversations about the U.S. presidential election, compared with 0.70 by McAuley and Leskovec (2013), which only considers topics.
in addition to using 75% of conversation history, we also extract the first 25% and 50% of history as training.