Multimodal Dataset for Esports Game Situation Understanding and Commentary Generation

Khái niệm cốt lõi

This paper introduces a new multimodal dataset, Game-MUG, that combines game event logs, caster's speech transcripts, audience chats, and game audio to enable comprehensive understanding of esports game situations and generate engaging commentaries.

Tóm tắt

The paper introduces the Game-MUG dataset, which is a multimodal dataset for esports game situation understanding and commentary generation. The dataset includes data from League of Legends (LOL) live streams, such as:

• Game event logs: Covering 6 types of game events (Kill, Non-Epic Monster, Tower, Dragon, Plate, Nexus) across 216 matches.
• Caster's speech transcripts: 70,711 transcript sentences with an average duration of 12.2 seconds.
• Audience chats: 3,657,611 chat instances, including emotes and emojis to capture audience sentiment.
• Game audio features: Extracted using the Geneva Minimalistic Acoustic Parameter Set (GeMAPS) to represent emotional aspects.

The authors propose a joint integration framework that leverages this multimodal data to:

1. Understand the game situation by encoding text, audio, and previous game events using a multimodal transformer encoder.
2. Generate engaging commentaries by incorporating the game situation understanding into a pre-trained language model decoder.

Experiments show that the multimodal approach outperforms single-modal baselines in both game situation understanding and commentary generation tasks. Human evaluation also confirms the effectiveness of the proposed approach in producing more informative and coherent commentaries.

Thống kê

The dataset contains 15,221 game events across 216 matches, with an average of 70.47 events per match.
The most common event types are Kill (36.45%), Tower (18.98%), and Dragon (10.81%).
The dataset includes 70,711 caster's speech transcript sentences and 3,657,611 audience chat instances.

Trích dẫn

"The dynamic nature of esports makes the situation relatively complicated for average viewers. Esports broadcasting involves game expert casters, but the caster-dependent game commentary is not enough to fully understand the game situation."
"It will be richer by including diverse multimodal esports information, including audiences' talks/emotions, game audio, and game match event information."

Thông tin chi tiết chính được chắt lọc từ

Game-MUG: Multimodal Oriented Game Situation Understanding and Commentary Generation Dataset

by Zhihao Zhang... lúc arxiv.org 05-01-2024

https://arxiv.org/pdf/2404.19175.pdf

Yêu cầu sâu hơn

How can the proposed multimodal approach be extended to generate personalized commentaries that cater to the preferences and knowledge levels of different viewer segments?

The proposed multimodal approach can be extended to generate personalized commentaries by incorporating user profiling and preference modeling techniques. By analyzing user interactions, such as chat messages, viewing history, and engagement patterns, the model can create user profiles that capture individual preferences and knowledge levels. These profiles can then be used to tailor the commentary generation process to cater to the specific interests and expertise of different viewer segments.
To achieve this personalization, the model can utilize techniques such as collaborative filtering to recommend content based on similar users' preferences, content-based filtering to recommend content similar to what the user has interacted with before, and hybrid approaches that combine both collaborative and content-based methods. By integrating these personalized recommendation strategies into the commentary generation process, the model can deliver commentaries that resonate with each viewer segment, enhancing their overall viewing experience.

How can the proposed multimodal approach be extended to generate personalized commentaries that cater to the preferences and knowledge levels of different viewer segments?

The proposed multimodal approach can be extended to generate personalized commentaries by incorporating user profiling and preference modeling techniques. By analyzing user interactions, such as chat messages, viewing history, and engagement patterns, the model can create user profiles that capture individual preferences and knowledge levels. These profiles can then be used to tailor the commentary generation process to cater to the specific interests and expertise of different viewer segments.
To achieve this personalization, the model can utilize techniques such as collaborative filtering to recommend content based on similar users' preferences, content-based filtering to recommend content similar to what the user has interacted with before, and hybrid approaches that combine both collaborative and content-based methods. By integrating these personalized recommendation strategies into the commentary generation process, the model can deliver commentaries that resonate with each viewer segment, enhancing their overall viewing experience.

How can the proposed multimodal approach be extended to generate personalized commentaries that cater to the preferences and knowledge levels of different viewer segments?

The proposed multimodal approach can be extended to generate personalized commentaries by incorporating user profiling and preference modeling techniques. By analyzing user interactions, such as chat messages, viewing history, and engagement patterns, the model can create user profiles that capture individual preferences and knowledge levels. These profiles can then be used to tailor the commentary generation process to cater to the specific interests and expertise of different viewer segments.
To achieve this personalization, the model can utilize techniques such as collaborative filtering to recommend content based on similar users' preferences, content-based filtering to recommend content similar to what the user has interacted with before, and hybrid approaches that combine both collaborative and content-based methods. By integrating these personalized recommendation strategies into the commentary generation process, the model can deliver commentaries that resonate with each viewer segment, enhancing their overall viewing experience.