  We propose an approach for helping agents compose email replies to customer
requests. To enable that, we use LDA to extract latent topics from a collection
of email exchanges. We then use these latent topics to label our data,
obtaining a so-called "silver standard" topic labelling. We exploit this
labelled set to train a classifier to: (i) predict the topic distribution of
the entire agent's email response, based on features of the customer's email;
and (ii) predict the topic distribution of the next sentence in the agent's
reply, based on the customer's email features and on features of the agent's
current sentence. The experimental results on a large email collection from a
contact center in the tele- com domain show that the proposed ap- proach is
effective in predicting the best topic of the agent's next sentence. In 80% of
the cases, the correct topic is present among the top five recommended topics
(out of fifty possible ones). This shows the potential of this method to be
applied in an interactive setting, where the agent is presented a small list of
likely topics to choose from for the next sentence.
