Presents an open-source podcast annotation tool and a small annotated dataset (7 episodes, 1,960 utterances, 300 check-worthy claims) for claim detection and stance classification in English, Norwegian, and German.
F-coref: Fast, Accurate and Easy to Use Coreference Resolution
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abstract
We introduce fastcoref, a python package for fast, accurate, and easy-to-use English coreference resolution. The package is pip-installable, and allows two modes: an accurate mode based on the LingMess architecture, providing state-of-the-art coreference accuracy, and a substantially faster model, F-coref, which is the focus of this work. F-coref allows to process 2.8K OntoNotes documents in 25 seconds on a V100 GPU (compared to 6 minutes for the LingMess model, and to 12 minutes of the popular AllenNLP coreference model) with only a modest drop in accuracy. The fast speed is achieved through a combination of distillation of a compact model from the LingMess model, and an efficient batching implementation using a technique we call leftover batching. Our code is available at https://github.com/shon-otmazgin/fastcoref
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Annotation Tool and Dataset for Fact-Checking Podcasts
Presents an open-source podcast annotation tool and a small annotated dataset (7 episodes, 1,960 utterances, 300 check-worthy claims) for claim detection and stance classification in English, Norwegian, and German.