Papers by Daniela Stepanov
QANom: Question-Answer driven SRL for Nominalizations (2020.coling-main)
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Ayal Klein, Jonathan Mamou, Valentina Pyatkin, Daniela Stepanov, Hangfeng He, Dan Roth, Luke Zettlemoyer, Ido Dagan
| Challenge: | Traditionally, SRL annotations focus on verbal predicates, but other types of predicate are frequent in natural language. |
| Approach: | They propose a semantic scheme for capturing predicate-argument relations for nominalizations, termed QANom, using crowdsourcing and QA-driven annotations. |
| Outcome: | The proposed scheme outperforms existing annotations and is useful for downstream tasks. |
Controlled Crowdsourcing for High-Quality QA-SRL Annotation (2020.acl-main)
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Paul Roit, Ayal Klein, Daniela Stepanov, Jonathan Mamou, Julian Michael, Gabriel Stanovsky, Luke Zettlemoyer, Ido Dagan
| Challenge: | Question-answer driven Semantic Role Labeling (QA-SRL) is an open and natural flavour of SRL, potentially attainable from laymen. |
| Approach: | They propose a question-answer driven semantic role labeling approach that uses question-announced questions to label predicate-argument relationships. |
| Outcome: | The proposed method yields high-quality annotation with dramatically higher coverage, enabling future replicable research of natural semantic annotations. |
HappyDB: A Corpus of 100,000 Crowdsourced Happy Moments (L18-1)
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Akari Asai, Sara Evensen, Behzad Golshan, Alon Halevy, Vivian Li, Andrei Lopatenko, Daniela Stepanov, Yoshihiko Suhara, Wang-Chiew Tan, Yinzhan Xu
| Challenge: | Recent research has focused on developing technologies that help users incorporate the findings of the science of happiness into their daily lives. |
| Approach: | They crowd-sourced HappyDB, a corpus of 100,000 happy moments, and applied several state-of-the-art analysis techniques to analyze HappyDB. |
| Outcome: | The proposed technology can understand how people express their happy moments in text and analyze them using state-of-the-art techniques. |