# CS-lol: a Dataset of Viewer Comment with Scene in E-sports Live-streaming

arXiv: https://arxiv.org/abs/2301.06876 · HTML: https://mlsanity.com/paper/2301.06876/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 31 references that are in the map.
- First posted on arXiv 17 Jan 2023.

## Builds on (most cited first, 7 of 7)

- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://mlsanity.com/paper/2010.01057/) (2020)
- [A Survey on Deep Learning for Named Entity Recognition](https://mlsanity.com/paper/1812.09449/) (2018)
- [DGL-KE: Training Knowledge Graph Embeddings at Scale](https://mlsanity.com/paper/2004.08532/) (2020)
- [Fighting Game Commentator with Pitch and Loudness Adjustment Utilizing Highlight Cues](https://mlsanity.com/paper/2108.08112/) (2021)
- [Promoting Mental Well-Being for Audiences in a Live-Streaming Game by Highlight-Based Bullet Comments](https://mlsanity.com/paper/2108.08083/) (2021)
- [Sentence Punctuation for Collaborative Commentary Generation in Esports Live-Streaming](https://mlsanity.com/paper/2110.12416/) (2021)

## Built on by (most cited first, 2 of 2)

- [Game-MUG: Multimodal Oriented Game Situation Understanding and Commentary Generation Dataset](https://mlsanity.com/paper/2404.19175/) (2024)
- [From Multimodal Perception to Strategic Reasoning: A Survey on AI-Generated Game Commentary](https://mlsanity.com/paper/2506.17294/) (2025)

## Abstract (arXiv)

Billions of live-streaming viewers share their opinions on scenes they are watching in real-time and interact with the event, commentators as well as other viewers via text comments. Thus, there is necessary to explore viewers' comments with scenes in E-sport live-streaming events. In this paper, we developed CS-lol, a new large-scale dataset containing comments from viewers paired with descriptions of game scenes in E-sports live-streaming. Moreover, we propose a task, namely viewer comment retrieval, to retrieve the viewer comments for the scene of the live-streaming event. Results on a series of baseline retrieval methods derived from typical IR evaluation methods show our task as a challenging task. Finally, we release CS-lol and baseline implementation to the research community as a resource.