提出自锚定注意力模型，高效识别游戏聊天中的亲社会行为
Self-Anchored Attention Model for Sample-Efficient Classification of Prosocial Text Chat
- 用全训练集作锚点，缓解小样本下的分类难题
- 在《使命召唤》游戏中实现7.9%性能提升
- 首个面向低资源场景的亲社会行为自动识别系统
数百万玩家每日在竞技类在线游戏中通过游戏内聊天互动。以往研究主要关注使用自然语言处理技术检测少量有害内容以实现内容监管，但近期研究强调识别亲社会行为的重要性，其价值与识别负面互动相当。识别亲社会行为有助于分析、奖励和推广积极互动。然而，目前针对游戏聊天中亲社会行为的数据集、模型和资源极为有限。本文结合无监督发现与游戏领域专家协作，从游戏聊天中识别并分类亲社会行为，并提出一种新型自锚定注意力模型（SAAM），相较现有最佳方法提升7.9%。该方法将整个训练集作为“锚点”，在训练数据稀缺条件下显著提升模型性能。由此构建了首个面向游戏内聊天的亲社会行为自动化分类系统，尤其适用于大规模标注数据不可得的低资源场景。本方法应用于最受欢迎的在线游戏之一《使命召唤：现代战争2》（Call of Duty(R): Modern Warfare(R)II），验证了其有效性。该研究首次将NLP技术用于发现与分类玩家游戏内聊天中的亲社会行为，有助于推动平台监管从单纯惩罚有害内容转向主动鼓励正向互动。
原文摘要 · Abstract (English)
Millions of players engage daily in competitive online games, communicating through in-game chat. Prior research has focused on detecting relatively small volumes of toxic content using various Natural Language Processing (NLP) techniques for the purpose of moderation. However, recent studies emphasize the importance of detecting prosocial communication, which can be as crucial as identifying toxic interactions. Recognizing prosocial behavior allows for its analysis, rewarding, and promotion. Unlike toxicity, there are limited datasets, models, and resources for identifying prosocial behaviors in game-chat text. In this work, we employed unsupervised discovery combined with game domain expert collaboration to identify and categorize prosocial player behaviors from game chat. We further propose a novel Self-Anchored Attention Model (SAAM) which gives 7.9% improvement compared to the best existing technique. The approach utilizes the entire training set as "anchors" to help improve model performance under the scarcity of training data. This approach led to the development of the first automated system for classifying prosocial behaviors in in-game chats, particularly given the low-resource settings where large-scale labeled data is not available. Our methodology was applied to one of the most popular online gaming titles - Call of Duty(R): Modern Warfare(R)II, showcasing its effectiveness. This research is novel in applying NLP techniques to discover and classify prosocial behaviors in player in-game chat communication. It can help shift the focus of moderation from solely penalizing toxicity to actively encouraging positive interactions on online platforms.
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