| Challenge: | condescending language use can bring dialogues to an end and disrupt healthy communities. |
| Approach: | They propose a model that uses a language-only model to model condescending linguistic acts in context. |
| Outcome: | a new model of condescending language use improves performance and motivates techniques . the model can estimate condescension rates in various online communities and relate these differences to community norms . |
Similar Papers
Pre-Training Language Models for Identifying Patronizing and Condescending Language: An Analysis (2022.lrec-1)
Copied to clipboard
| Challenge: | Patronizing and Condescending Language (PCL) is a subtle but harmful type of discourse. |
| Approach: | They propose to pre-train PCL detection models on other NLP tasks to improve their detection . they find that performance gains are possible when pre-training on sentiment, harmful language and commonsense morality. |
| Outcome: | The proposed models improve on pre-training on other NLP tasks focusing on sentiment, harmful language and commonsense morality, compared with tasks concentrating on political speech and social justice, the authors show . |
Detecting Community Sensitive Norm Violations in Online Conversations (2021.findings-emnlp)
Copied to clipboard
Chan Young Park, Julia Mendelsohn, Karthik Radhakrishnan, Kinjal Jain, Tushar Kanakagiri, David Jurgens, Yulia Tsvetkov
| Challenge: | Existing efforts to identify unacceptable behavior have focused on toxicity as the sole form of community norm violation. |
| Approach: | They propose a dataset that focuses on a more complete spectrum of community norms and their violations in local conversational and global contexts. |
| Outcome: | The proposed model improves the detection of community norm violations in local conversational and global contexts. |
Humans Need Context, What about Machines? Investigating Conversational Context in Abusive Language Detection (2024.lrec-main)
Copied to clipboard
| Challenge: | In this paper, we examine the role of conversational context in abusive language detection . prior studies have ignored the contextual nature of abusive language, ignoring this aspect . toxicity, hate speech, harmful stereotypes are among the forms of harmful language . |
| Approach: | They propose to use conversational context to analyze abusive language detection using two methods . they use "abusive language" as an umbrella term to refer to various forms of harmful language . |
| Outcome: | The proposed approach is based on two datasets in English and a new dataset of French tweets annotated for hate speech and stereotypes. |
Don’t Patronize Me! An Annotated Dataset with Patronizing and Condescending Language towards Vulnerable Communities (2020.coling-main)
Copied to clipboard
| Challenge: | a new dataset is proposed to help develop NLP models to categorize language that is patronizing or condescending towards vulnerable communities. |
| Approach: | They propose to annotate a dataset to help develop NLP models to categorize language that is patronizing or condescending towards vulnerable communities. |
| Outcome: | The proposed dataset supports the development of NLP models to categorize language that is patronizing or condescending towards vulnerable communities. |
PclGPT: A Large Language Model for Patronizing and Condescending Language Detection (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Patronizing and condescending language is an essential branch of toxic language . pre-trained language models perform poorly in detecting PCL due to its implicit toxicity traits . |
| Approach: | They propose a novel LLM benchmark for patronizing and condescending language . they use a dataset to analyze the toxicity of patronizing condescending languages . |
| Outcome: | The proposed model can detect patronizing and condescending language (PCL) the model can be used to analyze the toxicity of the language and to improve the detection. |
WikiConv: A Corpus of the Complete Conversational History of a Large Online Collaborative Community (D18-1)
Copied to clipboard
Yiqing Hua, Cristian Danescu-Niculescu-Mizil, Dario Taraborelli, Nithum Thain, Jeffery Sorensen, Lucas Dixon
| Challenge: | Compared to large-scale collections of conversations from social media, Wikipedia talk pages only capture a subset of all discussions and only accounts for the final form of each conversation. |
| Approach: | They propose to reconstruct a corpus that encompasses the complete history of conversations between Wikipedia contributors. |
| Outcome: | The proposed corpus extracts high quality data in both Chinese and English. |
Can Language Model Moderators Improve the Health of Online Discourse? (2024.naacl-long)
Copied to clipboard
Hyundong Cho, Shuai Liu, Taiwei Shi, Darpan Jain, Basem Rizk, Yuyang Huang, Zixun Lu, Nuan Wen, Jonathan Gratch, Emilio Ferrara, Jonathan May
| Challenge: | Existing efforts to automate conversational moderation have focused on banning harmful comments or deleting them, but such efforts can inadvertently push users towards echo chambers that exacerbate polarization. |
| Approach: | They propose a framework to assess models’ moderation capabilities independently of human intervention and propose 'conversational moderation' they propose to use language models as conversational moderators to provide specific feedback on toxic behavior but struggle to influence users to increase their levels of respect and cooperation. |
| Outcome: | The proposed framework assesses models’ moderation capabilities independently of human intervention and shows that appropriately prompted models provide specific and fair feedback on toxic behavior but struggle to influence users to increase their levels of respect and cooperation. |
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) (2025.acl-demo)
Copied to clipboard
| Challenge: | ACL 2025 System Demonstration Track accepted 64 papers based on reviews . short-listed 7 papers for Best System Demo award . |
| Approach: | the ACL 2025 System Demonstration Track is a conference for papers describing system demonstrations . the track received a record 187 submissions, of which 178 papers were valid with required materials . |
| Outcome: | the ACL 2025 System Demonstration Track received 187 submissions . 178 papers were valid with required materials . |
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) (2026.acl-demo)
Copied to clipboard
| Challenge: | ACL 2026 System Demonstration Track accepted 85 papers . one paper received Best Demo award . |
| Approach: | the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) took place from July 2-7, 2026 in San Diego, California. |
| Outcome: | the ACL 2026 System Demonstration Track accepted 85 papers based on the submitted reviews . one paper received the best demo award: The olmOCR Project: Building Fully Open OCR using VLMs . |
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) (2024.acl-demos)
Copied to clipboard
| Challenge: | ACL 2024 System Demonstration Track invites submissions describing system demonstrations . submissions will undergo a single-blind review process . |
| Approach: | the ACL 2024 System Demonstration Track invites submissions . papers will be published in a companion volume of the conference proceedings . submissions will undergo a single-blind review process . |
| Outcome: | the Demonstration Track at ACL 2024 is a venue for papers describing system demonstrations . publicly available open-source or open-access systems are of special interest . submissions will undergo a single-blind review process . |