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

Please check the official repository for more details and updates.

Experiments

Model	Runtime	Memory
Joshi et al. (2020)	12:06	27.4
Otmazgin et al. (2022)	06:43	4.6
+ Batching	06:00	6.6
Kirstain et al. (2021)	04:37	4.4
Dobrovolskii (2021)	03:49	3.5
F-Coref	00:45	3.3
+ Batching	00:35	4.5
+ Leftovers batching	00:25	4.0
The inference time(Min:Sec) and memory(GiB) for each model on 2.8K documents. Average of 3 runs. Hardware, NVIDIA Tesla V100 SXM2.

Citation

@inproceedings{Otmazgin2022FcorefFA,
  title={F-coref: Fast, Accurate and Easy to Use Coreference Resolution},
  author={Shon Otmazgin and Arie Cattan and Yoav Goldberg},
  booktitle={AACL},
  year={2022}
}

F-coref: Fast, Accurate and Easy to Use Coreference Resolution (Otmazgin et al., AACL-IJCNLP 2022)

biu-nlp/f-coref

F-Coref: Fast, Accurate and Easy to Use Coreference Resolution

Experiments

Citation

biu-nlp/
f-coref