# Topic Spotting using Hierarchical Networks with Self Attention

## TL;DR

This paper introduces a hierarchical self-attention model for topic spotting in dialogues, improving real-time conversation understanding and engagement in dialogue systems.

## Contribution

The paper presents a novel hierarchical self-attention model that outperforms previous methods in topic spotting, including in online, real-time scenarios.

## Key findings

- Superior performance on Switchboard corpus
- Effective in real-time online settings
- Generalizes well with limited dialog information

## Abstract

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.