# Topic-time Heatmaps for Human-in-the-loop Topic Detection and Tracking

arXiv: https://arxiv.org/abs/2110.07337 · HTML: https://mlsanity.com/paper/2110.07337/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 29 references that are in the map.
- First posted on arXiv 12 Oct 2021.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [In Defense of the Triplet Loss for Person Re-Identification](https://mlsanity.com/paper/1703.07737/) (2017)
- [Time2Vec: Learning a Vector Representation of Time](https://mlsanity.com/paper/1907.05321/) (2019)
- [Learning from Dialogue after Deployment: Feed Yourself, Chatbot!](https://mlsanity.com/paper/1901.05415/) (2019)
- [Can Neural Machine Translation be Improved with User Feedback?](https://mlsanity.com/paper/1804.05958/) (2018)
- [Dialogue Learning with Human Teaching and Feedback in End-to-End Trainable Task-Oriented Dialogue Systems](https://mlsanity.com/paper/1804.06512/) (2018)
- [Dialogue Learning With Human-In-The-Loop](https://mlsanity.com/paper/1611.09823/) (2016)
- [Putting Humans in the Natural Language Processing Loop: A Survey](https://mlsanity.com/paper/2103.04044/) (2021)
- [Multilingual Clustering of Streaming News](https://mlsanity.com/paper/1809.00540/) (2018)
- [Event-Driven News Stream Clustering using Entity-Aware Contextual Embeddings](https://mlsanity.com/paper/2101.11059/) (2021)
- [Why Didn't You Listen to Me? Comparing User Control of Human-in-the-Loop Topic Models](https://mlsanity.com/paper/1905.09864/) (2019)
- [ICE: Enabling Non-Experts to Build Models Interactively for Large-Scale Lopsided Problems](https://mlsanity.com/paper/1409.4814/) (2014)
- [Offline versus Online Triplet Mining based on Extreme Distances of Histopathology Patches](https://mlsanity.com/paper/2007.02200/) (2020)
- [TopicSifter: Interactive Search Space Reduction Through Targeted Topic Modeling](https://mlsanity.com/paper/1907.12079/) (2019)

## Abstract (arXiv)

The essential task of Topic Detection and Tracking (TDT) is to organize a collection of news media into clusters of stories that pertain to the same real-world event. To apply TDT models to practical applications such as search engines and discovery tools, human guidance is needed to pin down the scope of an "event" for the corpus of interest. In this work in progress, we explore a human-in-the-loop method that helps users iteratively fine-tune TDT algorithms so that both the algorithms and the users themselves better understand the nature of the events. We generate a visual overview of the entire corpus, allowing the user to select regions of interest from the overview, and then ask a series of questions to affirm (or reject) that the selected documents belong to the same event. The answers to these questions supplement the training data for the event similarity model that underlies the system.