Contextual Topic Modeling For Dialog Systems

By Chandra Khatri, Rahul Goel, Behnam Hedayatnia, Angeliki Metanillou, Anu Venkatesh, Raefer Gabriel, Arindam Mandal. 2018

26 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Natural Language Processing

Citations in the corpus

Towards Coherent and Engaging Spoken Dialog Response Generation Using Automatic Conversation Evaluators (2019, methodology): “Following this, we use model (Khatri et al., 2018)-predicted DAs (Stolcke et al.”
Advancing the State of the Art in Open Domain Dialog Systems through the Alexa Prize (2018, methodology): “More information about the model can be obtained at (Khatri et al., 2018b).”