# CONDA: a CONtextual Dual-Annotated dataset for in-game toxicity understanding and detection

arXiv: https://arxiv.org/abs/2106.06213 · HTML: https://mlsanity.com/paper/2106.06213/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 32 references that are in the map.
- First posted on arXiv 11 Jun 2021.

## Builds on (most cited first, 22 of 22)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling](https://mlsanity.com/paper/1810.00278/) (2018)
- [Automated Hate Speech Detection and the Problem of Offensive Language](https://mlsanity.com/paper/1703.04009/) (2017)
- [Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior](https://mlsanity.com/paper/1802.00393/) (2018)
- [Hate Speech Dataset from a White Supremacy Forum](https://mlsanity.com/paper/1809.04444/) (2018)
- [Key-Value Retrieval Networks for Task-Oriented Dialogue](https://mlsanity.com/paper/1705.05414/) (2017)
- [Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling](https://mlsanity.com/paper/1609.01454/) (2016)
- [A Benchmark Dataset for Learning to Intervene in Online Hate Speech](https://mlsanity.com/paper/1909.04251/) (2019)
- [Cross-Lingual Transfer Learning for Multilingual Task Oriented Dialog](https://mlsanity.com/paper/1810.13327/) (2018)
- [Semantic Parsing for Task Oriented Dialog using Hierarchical Representations](https://mlsanity.com/paper/1810.07942/) (2018)
- [Toxicity Detection: Does Context Really Matter?](https://mlsanity.com/paper/2006.00998/) (2020)
- [Conversations Gone Awry: Detecting Early Signs of Conversational Failure](https://mlsanity.com/paper/1805.05345/) (2018)
- [Joint Slot Filling and Intent Detection via Capsule Neural Networks](https://mlsanity.com/paper/1812.09471/) (2018)
- [Detecting Online Hate Speech Using Context Aware Models](https://mlsanity.com/paper/1710.07395/) (2017)
- [A Bi-model based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling](https://mlsanity.com/paper/1812.10235/) (2018)
- [Peer to Peer Hate: Hate Speech Instigators and Their Targets](https://mlsanity.com/paper/1804.04649/) (2018)
- [Multi-lingual Intent Detection and Slot Filling in a Joint BERT-based Model](https://mlsanity.com/paper/1907.02884/) (2019)
- [Detect All Abuse! Toward Universal Abusive Language Detection Models](https://mlsanity.com/paper/2010.03776/) (2020)
- [Exploring Cyberbullying and Other Toxic Behavior in Team Competition Online Games](https://mlsanity.com/paper/1504.02305/) (2015)
- [CASA-NLU: Context-Aware Self-Attentive Natural Language Understanding for Task-Oriented Chatbots](https://mlsanity.com/paper/1909.08705/) (2019)
- [Machine learning and semantic analysis of in-game chat for cyberbullying](https://mlsanity.com/paper/1907.10855/) (2019)
- [STFU NOOB! Predicting Crowdsourced Decisions on Toxic Behavior in Online Games](https://mlsanity.com/paper/1404.5905/) (2014)

## Built on by (most cited first, 14 of 14)

- [K-MHaS: A Multi-label Hate Speech Detection Dataset in Korean Online News Comment](https://mlsanity.com/paper/2208.10684/) (2022)
- [Towards Detecting Contextual Real-Time Toxicity for In-Game Chat](https://mlsanity.com/paper/2310.18330/) (2023)
- [COBIAS: Assessing the Contextual Reliability of Bias Benchmarks for Language Models](https://mlsanity.com/paper/2402.14889/) (2024)
- [In-game Toxic Language Detection: Shared Task and Attention Residuals](https://mlsanity.com/paper/2211.05995/) (2022)
- [Tri-level Joint Natural Language Understanding for Multi-turn Conversational Datasets](https://mlsanity.com/paper/2305.17729/) (2023)
- [Prosocial Behavior Detection in Player Game Chat: From Aligning Human-AI Definitions to Efficient Annotation at Scale](https://mlsanity.com/paper/2508.05938/) (2025)
- [IntentGrasp: A Comprehensive Benchmark for Intent Understanding](https://mlsanity.com/paper/2605.06832/) (2026)
- [Spoken Language Understanding for Conversational AI: Recent Advances and Future Direction](https://mlsanity.com/paper/2212.10728/) (2022)
- [MC-DRE: Multi-Aspect Cross Integration for Drug Event/Entity Extraction](https://mlsanity.com/paper/2308.06546/) (2023)
- [Textual Toxicity in Social Media: Understanding the Bangla Toxic Language Expressed in Facebook Comment](https://mlsanity.com/paper/2312.05467/) (2023)
- [Context-Aware Toxicity Detection in Multiplayer Games: Integrating Domain-Adaptive Pretraining and Match Metadata](https://mlsanity.com/paper/2504.01534/) (2025)
- ["bot lane noob" Towards Deployment of NLP-based Toxicity Detectors in Video Games](https://mlsanity.com/paper/2604.10175/) (2026)
- [On the Limits of LLM Adaptability: Impact of Model-Internalized Priors on Annotation Task Performance](https://mlsanity.com/paper/2606.00467/) (2026)
- [In-game Toxic Detection: Bi-directional Representations with Attention Residuals](https://mlsanity.com/paper/2609.34584/) (2026)

## Abstract (arXiv)

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.