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@@ -52,19 +52,10 @@ The inference time(Min:Sec) and memory(GiB) for each model on 2.8K documents. Av
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  ### Citation
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- ```
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- @inproceedings{otmazgin-etal-2022-f,
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- title = "{F}-coref: Fast, Accurate and Easy to Use Coreference Resolution",
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- author = "Otmazgin, Shon and
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- Cattan, Arie and
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- Goldberg, Yoav",
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- booktitle = "Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing: System Demonstrations",
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- month = nov,
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- year = "2022",
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- address = "Taipei, Taiwan",
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- publisher = "Association for Computational Linguistics",
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- url = "https://aclanthology.org/2022.aacl-demo.6",
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- pages = "48--56",
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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. https://github.com/shon-otmazgin/fastcoref",
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  }
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  ```
 
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  ### Citation
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+ @inproceedings{Otmazgin2022FcorefFA,
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+ title={F-coref: Fast, Accurate and Easy to Use Coreference Resolution},
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+ author={Shon Otmazgin and Arie Cattan and Yoav Goldberg},
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+ booktitle={AACL},
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+ year={2022}
 
 
 
 
 
 
 
 
 
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  }
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  ```