Papers by Magdalena Markowska
Re-Examining FactBank: Predicting the Author’s Presentation of Factuality (2022.coling-1)
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| Challenge: | Previously published results on FactBank are no longer valid. |
| Approach: | They propose to correct a subset of FactBank data to improve performance . they use multiple training paradigms, data smoothing techniques, and polarity classifiers . |
| Outcome: | The proposed model improves performance on the FactBank dataset. |
BeSt: The Belief and Sentiment Corpus (2022.lrec-1)
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Jennifer Tracey, Owen Rambow, Claire Cardie, Adam Dalton, Hoa Trang Dang, Mona Diab, Bonnie Dorr, Louise Guthrie, Magdalena Markowska, Smaranda Muresan, Vinodkumar Prabhakaran, Samira Shaikh, Tomek Strzalkowski
| Challenge: | a corpus of propositional content is a set of cognitive attitudes of different agents towards a text . propositional attitudes are a cognitive attitude, including belief and sentiment, towards . |
| Approach: | They propose a corpus which records cognitive state: who believes what, who has what sentiment . they use newswire and discussion forums in Chinese, English, and Spanish . |
| Outcome: | The proposed corpus records who believes what (i.e., factuality) and who has what sentiment towards what. |
Views Are My Own, but Also Yours: Benchmarking Theory of Mind Using Common Ground (2024.findings-acl)
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Adil Soubki, John Murzaku, Arash Yousefi Jordehi, Peter Zeng, Magdalena Markowska, Seyed Abolghasem Mirroshandel, Owen Rambow
| Challenge: | Existing benchmarks for theory of mind (ToM) use synthetic data, which can misalign with human behavior. |
| Approach: | They propose a question-answer benchmark based on naturally occurring spoken dialogs to evaluate theory of mind capabilities of language models. |
| Outcome: | The proposed dataset shows that LMs struggle to demonstrate theory of mind (ToM) . |
Finding Common Ground: Annotating and Predicting Common Ground in Spoken Conversations (2023.findings-emnlp)
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| Challenge: | Creating and updating common ground (CG) between interlocutors is the key to a successful conversation. |
| Approach: | They propose a new annotation and corpus to capture common ground in human communication . they then conduct experiments to extract propositions from dialog and track their status in common ground from the perspective of each speaker . |
| Outcome: | The proposed corpus captures common ground from the perspective of two speakers in a dialog. |