# TIAGE: A Benchmark for Topic-Shift Aware Dialog Modeling

arXiv: https://arxiv.org/abs/2109.04562 · HTML: https://mlsanity.com/paper/2109.04562/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 32 references that are in the map.
- First posted on arXiv 9 Sep 2021.

## Builds on (most cited first, 16 of 16)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://mlsanity.com/paper/1911.00536/) (2019)
- [Personalizing Dialogue Agents: I have a dog, do you have pets too?](https://mlsanity.com/paper/1801.07243/) (2018)
- [MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling](https://mlsanity.com/paper/1810.00278/) (2018)
- [Text Segmentation as a Supervised Learning Task](https://mlsanity.com/paper/1803.09337/) (2018)
- [Target-Guided Open-Domain Conversation](https://mlsanity.com/paper/1905.11553/) (2019)
- [Grounded Conversation Generation as Guided Traverses in Commonsense Knowledge Graphs](https://mlsanity.com/paper/1911.02707/) (2019)
- [Dialogue Learning with Human Teaching and Feedback in End-to-End Trainable Task-Oriented Dialogue Systems](https://mlsanity.com/paper/1804.06512/) (2018)
- [SECTOR: A Neural Model for Coherent Topic Segmentation and Classification](https://mlsanity.com/paper/1902.04793/) (2019)
- [Approximating Interactive Human Evaluation with Self-Play for Open-Domain Dialog Systems](https://mlsanity.com/paper/1906.09308/) (2019)
- [Two-Level Transformer and Auxiliary Coherence Modeling for Improved Text Segmentation](https://mlsanity.com/paper/2001.00891/) (2020)
- [Improving Robustness of Task Oriented Dialog Systems](https://mlsanity.com/paper/1911.05153/) (2019)
- [Generic Intent Representation in Web Search](https://mlsanity.com/paper/1907.10710/) (2019)
- [Sounding Board: A User-Centric and Content-Driven Social Chatbot](https://mlsanity.com/paper/1804.10202/) (2018)

## Built on by (most cited first, 22 of 22)

- [On Memory Construction and Retrieval for Personalized Conversational Agents](https://mlsanity.com/paper/2502.05589/) (2025)
- [A Survey on Proactive Dialogue Systems: Problems, Methods, and Prospects](https://mlsanity.com/paper/2305.02750/) (2023)
- [Neural Approaches to Conversational Information Retrieval](https://mlsanity.com/paper/2201.05176/) (2022)
- [Uncovering the Potential of ChatGPT for Discourse Analysis in Dialogue: An Empirical Study](https://mlsanity.com/paper/2305.08391/) (2023)
- [LLM Task Interference: An Initial Study on the Impact of Task-Switch in Conversational History](https://mlsanity.com/paper/2402.18216/) (2024)
- [Mind the Gap Between Conversations for Improved Long-Term Dialogue Generation](https://mlsanity.com/paper/2310.15415/) (2023)
- [SuperDialseg: A Large-scale Dataset for Supervised Dialogue Segmentation](https://mlsanity.com/paper/2305.08371/) (2023)
- [Multi-Granularity Prompts for Topic Shift Detection in Dialogue](https://mlsanity.com/paper/2305.14006/) (2023)
- [Recent Trends in Linear Text Segmentation: a Survey](https://mlsanity.com/paper/2411.16613/) (2024)
- [Taking a turn for the better: Conversation redirection throughout the course of mental-health therapy](https://mlsanity.com/paper/2410.07147/) (2024)
- [Topic Shift Detection in Chinese Dialogues: Corpus and Benchmark](https://mlsanity.com/paper/2305.01195/) (2023)
- [MP2D: An Automated Topic Shift Dialogue Generation Framework Leveraging Knowledge Graphs](https://mlsanity.com/paper/2403.05814/) (2024)
- [ES-Mem: Event Segmentation-Based Memory for Long-Term Dialogue Agents](https://mlsanity.com/paper/2601.07582/) (2026)
- [Unsupervised Mutual Learning of Discourse Parsing and Topic Segmentation in Dialogue](https://mlsanity.com/paper/2405.19799/) (2024)
- [Dynamics of Spontaneous Topic Changes in Next Token Prediction with Self-Attention](https://mlsanity.com/paper/2501.06382/) (2025)
- [Breaking the Stigma! Unobtrusively Probe Symptoms in Depression Disorder Diagnosis Dialogue](https://mlsanity.com/paper/2501.15260/) (2025)
- [PaRT: Enhancing Proactive Social Chatbots with Personalized Real-Time Retrieval](https://mlsanity.com/paper/2504.20624/) (2025)
- [Reinforced Approximate Exploratory Data Analysis](https://mlsanity.com/paper/2212.06225/) (2022)
- [When F1 Fails: Granularity-Aware Evaluation for Dialogue Topic Segmentation](https://mlsanity.com/paper/2512.17083/) (2025)
- [DASH: Dialogue-Aware Similarity and Handshake Recognition for Topic Segmentation in Public-Channel Conversations](https://mlsanity.com/paper/2512.15042/) (2025)
- [Beyond Continuity: Challenges of Context Switching in Multi-Turn Dialogue with LLMs](https://mlsanity.com/paper/2605.09268/) (2026)
- [CobSeg: Coherence Boundary Modeling for Dialogue Topic Segmentation](https://mlsanity.com/paper/2605.30668/) (2026)

## Abstract (arXiv)

Human conversations naturally evolve around different topics and fluently move between them. In research on dialog systems, the ability to actively and smoothly transition to new topics is often ignored. In this paper we introduce TIAGE, a new topic-shift aware dialog benchmark constructed utilizing human annotations on topic shifts. Based on TIAGE, we introduce three tasks to investigate different scenarios of topic-shift modeling in dialog settings: topic-shift detection, topic-shift triggered response generation and topic-aware dialog generation. Experiments on these tasks show that the topic-shift signals in TIAGE are useful for topic-shift response generation. On the other hand, dialog systems still struggle to decide when to change topic. This indicates further research is needed in topic-shift aware dialog modeling.