  Success of deep learning techniques have renewed the interest in development
of dialogue systems. However, current systems struggle to have consistent long
term conversations with the users and fail to build rapport. Topic spotting,
the task of automatically inferring the topic of a conversation, has been shown
to be helpful in making a dialog system more engaging and efficient. We propose
a hierarchical model with self attention for topic spotting. Experiments on the
Switchboard corpus show the superior performance of our model over previously
proposed techniques for topic spotting and deep models for text classification.
Additionally, in contrast to offline processing of dialog, we also analyze the
performance of our model in a more realistic setting i.e. in an online setting
where the topic is identified in real time as the dialog progresses. Results
show that our model is able to generalize even with limited information in the
online setting.
