Papers by Christine De Kock
Survival text regression for time-to-event prediction in conversations (2021.findings-acl)
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| Challenge: | a recent study has modelled time-to-event prediction tasks as classification tasks . authors: this is contrived and less informative than traditional classification models . |
| Approach: | They propose to frame time-to-event prediction tasks as classification tasks . they use survival regression techniques commonly used in healthcare and reliability engineering . |
| Outcome: | The proposed models outperform text regression methods and comparable classification models on three datasets. |
Leveraging Wikipedia article evolution for promotional tone detection (2022.acl-long)
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| Challenge: | Detecting biased language is useful for a variety of applications, authors say . a dataset for document-level promotional tone detection is available for WikiEvolve . |
| Approach: | They propose a dataset for document-level promotional tone detection using Wikipedia . they use a gradient reversal framework to encode two versions simultaneously . |
| Outcome: | The proposed dataset improves on in-domain and out-of-domain evaluations. |
I Beg to Differ: A study of constructive disagreement in online conversations (2021.eacl-main)
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| Challenge: | Disagreements are pervasive in human communication. |
| Approach: | They construct a corpus of Wikipedia Talk page conversations that contain content disputes and define the task of predicting whether disagreements will be escalated to mediation by a moderator. |
| Outcome: | The proposed model outperforms feature-based models in predicting whether disagreements will escalate to mediation by a moderator. |
How to disagree well: Investigating the dispute tactics used on Wikipedia (2022.emnlp-main)
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| Challenge: | Disagreements are often studied from the perspective of toxicity or analysing argument structure. |
| Approach: | They propose a dispute tactics framework which unifies both perspectives . they annotate 213 disagreements from Wikipedia Talk pages . |
| Outcome: | The proposed framework can be used to predict disagreements with a transformer-based model. |