Traditional toxicity detection models have focused on the single utterance
level without deeper understanding of context. We introduce CONDA, a new
dataset for in-game toxic language detection enabling joint intent
classification and slot filling analysis, which is the core task of Natural
Language Understanding (NLU). The dataset consists of 45K utterances from 12K
conversations from the chat logs of 1.9K completed Dota 2 matches. We propose a
robust dual semantic-level toxicity framework, which handles utterance and
token-level patterns, and rich contextual chatting history. Accompanying the
dataset is a thorough in-game toxicity analysis, which provides comprehensive
understanding of context at utterance, token, and dual levels. Inspired by NLU,
we also apply its metrics to the toxicity detection tasks for assessing
toxicity and game-specific aspects. We evaluate strong NLU models on CONDA,
providing fine-grained results for different intent classes and slot classes.
Furthermore, we examine the coverage of toxicity nature in our dataset by
comparing it with other toxicity datasets.

本文介绍 CONDA 数据集，它是一个包含了来自 1.9K 完成的 Dota 2 比赛聊天记录的 12K 个对话和 45K 个话语的数据集，用于进行游戏中毒性语言的检测和自然语言理解。同时提出了一种鲁棒的双语义级毒性框架，能够处理话语和单词级别的模式和上下文历史。评估了针对 CONDA 的强 NLU 模型，提供了不同意图类别和插槽类别的细粒度结果，并通过与其他毒性数据集的比较检查了本数据集中毒性的覆盖范围。