Shir Ashury Tahan — accepted AI-conference papers

2024

Data-driven Coreference-based Ontology Building

Shir Ashury Tahan, Amir David Nissan Cohen, Nadav Cohen, Yoram Louzoun, Yoav Goldberg

EMNLP 2024finding

While coreference resolution is traditionally used as a component in individual document understanding, in this work we take a more global view and explore what can we learn about a domain from the set of all document-level coreference relations that are present in a large corpus. We derive corefere…

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2024

Label-Efficient Model Selection for Text Generation

Shir Ashury Tahan, Ariel Gera, Benjamin Sznajder, Leshem Choshen, Liat Ein-Dor, Eyal Shnarch

ACL 2024long

Model selection for a given target task can be costly, as it may entail extensive annotation of the quality of outputs of different models. We introduce DiffUse, an efficient method to make an informed decision between candidate text generation models based on preference annotations. DiffUse reduces…

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