Edit model card

LingMess: Linguistically Informed Multi Expert Scorers for Coreference Resolution

LingMess is a linguistically motivated categorization of mention-pairs into 6 types of coreference decisions and learn a dedicated trainable scoring function for each category. This significantly improves the accuracy of the pairwise scorer as well as of the overall coreference performance on the English Ontonotes coreference corpus.

Please check the official repository for more details and updates.

Training on OntoNotes

We present the test results on OntoNotes 5.0 dataset.

Model Avg. F1
SpanBERT-large + e2e 79.6
Longformer-large + s2e 80.3
Longformer-large + LingMess 81.4

Citation

If you find LingMess useful for your work, please cite the following paper:

@misc{https://doi.org/10.48550/arxiv.2205.12644,
  doi = {10.48550/ARXIV.2205.12644},
  url = {https://arxiv.org/abs/2205.12644},
  author = {Otmazgin, Shon and Cattan, Arie and Goldberg, Yoav},
  keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {LingMess: Linguistically Informed Multi Expert Scorers for Coreference Resolution},
  publisher = {arXiv}, 
  year = {2022}, 
  copyright = {Creative Commons Attribution 4.0 International}
}
Downloads last month
451
Hosted inference API

Unable to determine this model’s pipeline type. Check the docs .

Space using biu-nlp/lingmess-coref 1

Evaluation results