# Contextual Topic Modeling For Dialog Systems

arXiv: https://arxiv.org/abs/1810.08135 · HTML: https://mlsanity.com/paper/1810.08135/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2018.

## Built on by (most cited first, 7 of 7)

- [Neural Approaches to Conversational Information Retrieval](https://mlsanity.com/paper/2201.05176/) (2022)
- [ChatGPT as your Personal Data Scientist](https://mlsanity.com/paper/2305.13657/) (2023)
- [Don't Take This Out of Context! On the Need for Contextual Models and Evaluations for Stylistic Rewriting](https://mlsanity.com/paper/2305.14755/) (2023)
- [Introducing "Forecast Utterance" for Conversational Data Science](https://mlsanity.com/paper/2309.03877/) (2023)
- [Social Commonsense-Guided Search Query Generation for Open-Domain Knowledge-Powered Conversations](https://mlsanity.com/paper/2310.14340/) (2023)
- [Open-domain Topic Identification of Out-of-domain Utterances using Wikipedia](https://mlsanity.com/paper/2101.11134/) (2021)
- [Chatbot System Architecture](https://mlsanity.com/paper/2201.06348/) (2022)

## Abstract (arXiv)

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.