MOBA - E 2 C : Generating MOBA Game Commentaries via Capturing Highlight Events from the Meta-Data (2022)

Abstract: MOBA (Multiplayer Online Battle Arena) games such as Dota2 are currently one of the most popular e-sports gaming genres. Following professional commentaries is a great way to understand and enjoy a MOBA game. However, massive game competitions lack commentaries because of the shortage of professional human commentators. As an alternative, employing machine commentators that can work at any time and place is a feasible solution. Considering the challenges in modeling MOBA games, we propose a data-driven MOBA commentary generation framework, MOBA-E2C, allowing a model to generate commentaries based on the game meta-data. Subsequently, to alleviate the burden of collecting supervised data, we propose a MOBA-FuseGPT generator to generate MOBA game commentaries by fusing the power of a rule-based generator and a generative GPT generator. Finally, in the experiments, we take a popular MOBA game Dota2 as our case and construct a Chinese Dota2 commentary generation dataset Dota2-Commentary. Experimental results demonstrate the superior performance of our approach. To the best of our knowledge, this work is the first Dota2 machine commentator and Dota2-Commentary is the first dataset.

## introduction with the development of the live-streaming service 1 and the e-sports industry, increasing game fans now are addicted to watching online live-streaming @xcite . in a live-streaming channel, besides the game video, a professional streamer makes commentaries to vividly narrate the game's progress @xcite ; thus, audiences can enjoy a game more interestingly and easily by following a game live-streaming, especially when they do not have enough background knowledge. nonetheless, not all e-sports game competitions can be narrated by live-streaming channels because 1) streamers often only select famous game competitions in their channels to attract more audiences; 2) unlike traditional sports such as basketball and football that have a high threshold for hosting and live-streaming, an e-sports game only requires several computers with the internet, and thus the number of e-sports game competitions is always larger than the number of live-streaming channels; 3) the game rules are complex @xcite and can be changed at any time; thus, only a few experienced people are qualified for this job. user preferences are diverse, and thus it is not trivial to provide professional commentaries to every game competition. a feasible way is to deploy machine commentators, which can work whenever and wherever possible in a low-cost way. however, building a machine commentator is challenging @xcite . first, it is unrealistic for machines to generate comments by watching videos like human beings because understanding a game video requires a great deal of technology and workforce. second, making commentaries to narrate e-sports games requires tremendous knowledge @xcite ; third, a machine commentator must have the ability to capture the highlight events of a game. consequently, we can hardly find such products on the market. this paper explores the research field of the game commentary generation and mainly focuses on moba (multiplayer online battle arena) games such as dota2 and league of legends. with the mentioned challenges in mind, we propose a data-driven moba game generation framework moba-e2c along with a moba-fusegpt generator. instead of making commentaries based on the visual feature (i.e., video), the commentaries are narrated based on the meta-data of a game. in short, moba-e2c first uses several types of event handlers to capture the highlight events that need to be narrated from the game's meta-data. each event uses a table of key-value attributes to record the necessary content, and then the game commentary generation problem can be subsequently regarded as a data-to-text generation task. collecting and constructing supervised data is thorny. finally, to generate high-quality commentaries and reduce the requirement of supervised data, our data-totext generator moba-fusegpt takes advantage of both the rule-based method and the pre-trained language modeling (i.e., gpt2). in experiments, a popular moba game dota2 @xcite ) is adopted as the case. we first designed 34 different event handlers for moba-e2c to capture highlight game events. subsequently, we collected and constructed a chinese dota2 commentary generation dataset dota2-commentary, which includes 234 recent dota2 game sessions 2 and 7,473 commentaries generated by professional human annotators. experimental results have shown our approach has great improvement in both the default scenario and the few/zero-shot scenario. to the best of our knowledge, this work is the first dota2 machine commentator, and dota2-commentary is the first dataset. the contribution of this work is four-fold: ## moba-e2c instead of using visual features, we propose a data-driven framework moba-e2c (e2c: eventto-commentary) to generate commentaries from the meta-data of a game. ## experiment this paper takes dota2, one of the most popular moba games, as our case. this section describes the construction of the dota2 implementation of moba-e2c and the dataset dota2-commentary. ## related work the development of e-sports has greatly enriched people's amateur life and brought great commercial value. subsequently, many ai-based tools/models have been developed for e-sports to meet the increasingly growing demands. the most popular applications include: 1) predicting the outcome/death @xcite @xcite @xcite ; 2) identifying the player/role @xcite ; 3) recommending items/heroes/characters @xcite @xcite ; 4) ai players (openai, 2019); and many others @xcite @xcite . compared to such works, we study the machine commentary generation, which not only involves the knowledge of e-sports but also the nlp techniques. text generation is an important task in both the academic and industry @xcite . generally, it learns to generate texts based on the given data @xcite , such as generating biology based on infobox @xcite , generating descriptions @xcite , and many others. only recently, there is a work to generate commentaries for traditional sports games @xcite . however, no previous work has tried to generate moba commentary because e-sports games are pretty complex, and it is not easy to collect supervised training data. compared to such works, this work focuses on a more specific task, namely, generating game commentaries. ## conclusion this paper proposes constructing machine game commentators that can work at any time and place. this paper focuses on the moba games and proposes a novel data-driven commentary generation framework moba-e2c. instead of using visual features, moba-e2c generates commentaries by using the meta-data of a game. moba-e2c regards each game session as a live-streaming sequence of game states, and then employs several event handlers to capture events. subsequently, we use moba-fusegpt to generate commentaries based on the identified events. it infuses the advantages of rule-based methods and the generative pre-trained language model. in the experiments, we take dota2 as the case study and construct a dataset dota2-commentary. extensive experiments have demonstrated the effectiveness of our approach. limitation and future work this work focuses on exploring the research field of moba game machine generators and setting the baseline; thus, we propose a data-driven framework moba-e2c, a generator moba-fusegpt, and a dataset dota2-commentary. however, it can be easily found that this work did not try to use a new network architecture. in the future, based on the foundation of this work, we will continue to investigate the potential of moba game machine generator and brings more brilliant models.