Papers by Carla Perez Almendros
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 . |
Do Large Language Models Understand Mansplaining? Well, Actually... (2024.lrec-main)
Copied to clipboard
| Challenge: | Gender bias has been studied by the NLP community, but other variations of it, such as mansplaining, have received little attention. |
| Approach: | They propose to analyze a corpus of 886 mansplaining stories experienced by women and examine how Large Language Models can understand and identify mansplaiting. |
| Outcome: | The proposed models reproduce some of the social patterns behind mansplaining situations by praising men for giving unsolicited advice to women. |
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. |