Contextual Topic Modeling For Dialog Systems
This work addresses the need for coherent and engaging dialog systems by improving topic modeling for chatbots, though it is incremental as it builds on existing neural and unsupervised methods.
The paper tackled the problem of predicting conversation topics in free-form human-chatbot dialogs by incorporating conversational context and dialog act features, resulting in relative gains of 35% in topic classification accuracy and 11% in unsupervised keyword detection recall.
Accurate prediction of conversation topics can be a valuable signal for creating coherent and engaging dialog systems. In this work, we focus on context-aware topic classification methods for identifying topics in free-form human-chatbot dialogs. We extend previous work on neural topic classification and unsupervised topic keyword detection by incorporating conversational context and dialog act features. On annotated data, we show that incorporating context and dialog acts leads to relative gains in topic classification accuracy by 35% and on unsupervised keyword detection recall by 11% for conversational interactions where topics frequently span multiple utterances. We show that topical metrics such as topical depth is highly correlated with dialog evaluation metrics such as coherence and engagement implying that conversational topic models can predict user satisfaction. Our work for detecting conversation topics and keywords can be used to guide chatbots towards coherent dialog.