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--- |
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annotations_creators: |
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- expert-generated |
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language: |
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- en |
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language_creators: |
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- found |
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license: |
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- afl-3.0 |
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multilinguality: |
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- monolingual |
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pretty_name: CONDA |
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size_categories: |
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- 10K<n<100K |
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source_datasets: |
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- original |
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tags: |
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- CONDA |
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task_categories: |
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- text-classification |
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- token-classification |
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task_ids: |
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- intent-classification |
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--- |
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# Dataset Card for CONDA |
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## Table of Contents |
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- [Dataset Description](#dataset-description) |
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- [Abstract](#dataset-summary) |
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- [Leaderboards](#leaderboards) |
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- [Evaluation Metrics](#evaluation-metrics) |
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- [Languages](#languages) |
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- [Video](#video) |
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- [Citation Information](#citation-information) |
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## Dataset Description |
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- **Homepage:** [CONDA](https://github.com/usydnlp/CONDA) |
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- **Paper:** [CONDA: a CONtextual Dual-Annotated dataset for in-game toxicity understanding and detection](https://arxiv.org/abs/2106.06213) |
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- **Point of Contact:** [Caren Han](caren.han@sydney.edu.au) |
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## Dataset Summary |
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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. |
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## Leaderboards |
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The Codalab leaderboard can be found at: https://codalab.lisn.upsaclay.fr/competitions/7827 |
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### Evaluation Metrics |
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**JSA**(Joint Semantic Accuracy) is used for ranking. An utterance is deemed correctly analysed only if both utterance-level and all the token-level labels including Os are correctly predicted. |
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Besides, the f1 score of **utterance-level** E(xplicit) and I(mplicit) classes, **token-level** T(oxicity), D(ota-specific), S(game Slang) classes will be shown on the leaderboard (but not used as the ranking metric). |
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## Languages |
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English |
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## Video |
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Please enjoy a video presentation covering the main points from our paper: |
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<p align="centre"> |
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[](https://www.youtube.com/watch?v=qRCPSSUuf18) |
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</p> |
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## Citation Information |
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``` |
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@inproceedings{weld-etal-2021-conda, |
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title = "{CONDA}: a {CON}textual Dual-Annotated dataset for in-game toxicity understanding and detection", |
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author = "Weld, Henry and |
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Huang, Guanghao and |
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Lee, Jean and |
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Zhang, Tongshu and |
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Wang, Kunze and |
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Guo, Xinghong and |
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Long, Siqu and |
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Poon, Josiah and |
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Han, Caren", |
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booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021", |
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month = aug, |
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year = "2021", |
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address = "Online", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2021.findings-acl.213", |
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doi = "10.18653/v1/2021.findings-acl.213", |
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pages = "2406--2416", |
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} |
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``` |
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