At run-time, however, we construct the conversation history for the tuning and test sets incrementally, one utterance at a time, mirroring a real-world scenario where our knowledge is limited to the utterances that have been spoken up to that point in time.
each development/test utterance is associated with a different conversation history d * , for which we infer a topic distribution θ d * = p(z k |d * ) using the trained LDA model.