CS-lol: a Dataset of Viewer Comment with Scene in E-sports Live-streaming
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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.
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
7 of 31 references.
References · 7
- 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
- 2020LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attentioncited by 326
- 2018A Survey on Deep Learning for Named Entity Recognitioncited by 282
- 2020DGL-KE: Training Knowledge Graph Embeddings at Scalecited by 67
- 2021Fighting Game Commentator with Pitch and Loudness Adjustment Utilizing Highlight Cuescited by 4
- 2021Promoting Mental Well-Being for Audiences in a Live-Streaming Game by Highlight-Based Bullet Commentscited by 1
- 2021Sentence Punctuation for Collaborative Commentary Generation in Esports Live-Streamingcited by 1
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Citing papers per year: 2024: 1; 2025: 1.
2 papers.