Assisting Composition of Email Responses: a Topic Prediction Approach
This addresses the efficiency of customer service agents in telecom contact centers by providing topic suggestions, though it is incremental as it builds on existing topic modeling and classification methods.
The paper tackles the problem of assisting agents in composing email replies by predicting the topic of the next sentence based on customer emails and current agent sentences, achieving that the correct topic is among the top five recommendations in 80% of cases out of fifty possible topics.
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.