MCS : An In-battle Commentary System for MOBA Games (2022)

Abstract: This paper introduces a generative system for in-battle real-time commentary in mobile MOBA games. Event commentary is important for battles in MOBA games, which is applicable to a wide range of scenarios like live streaming, e-sports commentary and combat information analysis. The system takes real-time match statistics and events as input, and an effective transform method is designed to convert match statistics and utterances into consistent encoding space. This paper presents the general framework and implementation details of the proposed system, and provides experimental results on large-scale real-world match data.

## introduction in recent years, moba (multiplayer online battle arena) games have been popular all over the world. as a popular mobile moba game, honor of kings has a dau of over 100 million, and has become an official sport in the 2022 hangzhou asian games. in moba games, several players are divided into two teams and carry out a battle against each other. ai reseach in moba games has received many attention @xcite . real-time commentary plays an important role in live streaming of e-sports competitions, assists the audience in the comprehension of game situation and events, expanding the presence of moba games. in-battle commentary aims at generating coherent and informative natural language descriptions based on game situation, match statistics and player behaviour, as illustrated in figure 1 . similar systems have already been widely applied in realworld applications such as automated news broadcast, television sports commentary, online video description, etc @xcite @xcite @xcite . in this paper, we focus on the scenario of live e-sports commentary killer hero: shangxiang sun be killed hero: yuanfang li event: double kill commentary: hero kills deaths assists ⋯ gold gold (variation) shangxiang sun 12 4 7 ⋯ 14028 16.23/second que bian 3 2 20 ⋯ 13428 19.53/second ⋯ ⋯ ⋯ ⋯ ⋯ ⋯ ⋯ ying yun 8 6 8 ⋯ 15186 3.73/second yuanfang li 9 7 8 ⋯ 12186 3.73/second ⋯ ⋯ ⋯ ⋯ ⋯ ⋯ ⋯ 孙尚香拿下双杀，厉害！ shangxiang sun accomplished double kill, awesome! in mobile moba games and introduce mcs: an in-battle commentary generation system 12 . automatic commentary is grounded in in-battle match statistics and events, regarded as a data-totext generation task. recently, many large-scale language models have been proposed and promoted the advance in natural language generation @xcite @xcite . these pre-trained language models are also beneficial for data-to-text tasks @xcite , in this work, we employ openai's gpt-2 model as the basic generator, and build a neural pipelined approach for commentary generation. although recent works have shown promising results regarding live comment generation @xcite @xcite @xcite , there are still many challenges for constructing a commentary generation model for moba games: (1) previous studies incorporated static data like final scores, while live e-sports commentary uses changing match statistics as the game goes on. current statistics as well as history statistics are both vital for text generation. for example, the narrowing of advantages may cause anxiety, while overturn from deficit can be ## system framework we first introduce the general framework of the proposed mcs system. the system consists of three modules in a pipeline, as presented in figure 2 . taking in-battle statistics as input, the system produces a descriptive sentence based on current and history data. ## system implementation in this section, the implementation details of the proposed three modules are provided. in general, the network architecture are constructed following the pipeline of language models. as described in figure 4 , features are serialized and fed as the inputs, the network predicts a target hero id and generates a comment as the outputs. ## materials and results the structured data and commentary history of 30 million matches in professional leagues and live broadcasts of honor of kings were collected and used to train and evaluate the proposed system. perplexity is used to evaluate the fluency of generated sentences, rouge-1, rouge-2 and rouge-l are used to measure the distance between generated sentences and ground truth. we also calculate the generation validity by human evaluation. ## conclusion we propose mcs, a model for in-battle real-time commentary generation in moba games. mo-ba is a type of games popular all over the world, commentary plays an important role in game comprehension for audience. the proposed mcs is designed with the ability of analyzing real-time match statistics and generating coherent descriptive contents. it has a wide range of applications such as e-sports commentary, combat information analysis, etc, and has the potential to promote game development and improve match environment.