# An Empirical Study of Topic Transition in Dialogue

arXiv: https://arxiv.org/abs/2111.14188 · HTML: https://mlsanity.com/paper/2111.14188/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 35 references that are in the map.
- First posted on arXiv 28 Nov 2021.

## Builds on (most cited first, 4 of 4)

- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Deep Learning Based Text Classification: A Comprehensive Review](https://mlsanity.com/paper/2004.03705/) (2020)
- [OTTers: One-turn Topic Transitions for Open-Domain Dialogue](https://mlsanity.com/paper/2105.13710/) (2021)
- [Improving Unsupervised Dialogue Topic Segmentation with Utterance-Pair Coherence Scoring](https://mlsanity.com/paper/2106.06719/) (2021)

## Built on by (most cited first, 4 of 4)

- [MP2D: An Automated Topic Shift Dialogue Generation Framework Leveraging Knowledge Graphs](https://mlsanity.com/paper/2403.05814/) (2024)
- [SEADialogues: A Multilingual Culturally Grounded Multi-turn Dialogue Dataset on Southeast Asian Languages](https://mlsanity.com/paper/2508.07069/) (2025)
- [Dynamics of Spontaneous Topic Changes in Next Token Prediction with Self-Attention](https://mlsanity.com/paper/2501.06382/) (2025)
- [Beyond Continuity: Challenges of Context Switching in Multi-Turn Dialogue with LLMs](https://mlsanity.com/paper/2605.09268/) (2026)

## Abstract (arXiv)

Transitioning between topics is a natural component of human-human dialog. Although topic transition has been studied in dialogue for decades, only a handful of corpora based studies have been performed to investigate the subtleties of topic transitions. Thus, this study annotates 215 conversations from the switchboard corpus and investigates how variables such as length, number of topic transitions, topic transitions share by participants and turns/topic are related. This work presents an empirical study on topic transition in switchboard corpus followed by modelling topic transition with a precision of 83% for in-domain(id) test set and 82% on 10 out-of-domain}(ood) test set. It is envisioned that this work will help in emulating human-human like topic transition in open-domain dialog systems.