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Deep Neural Networks for Coreference Resolution for Polish

Summary

This work presents the first coreference resolver for Polish built on deep neural networks. Coreference resolution means grouping text fragments that point to the same discourse entity. The authors train a feed-forward classifier that scores pairs of mentions and returns the probability that they corefer. Mentions are then joined into chains by either a mention-based or an entity-based clustering step, with a tunable linking threshold.

The baseline input combines 50-dimensional word embeddings, mention-type flags and pair-level cues such as distance, string identity and acronym matching. The authors then test several variants:

  • 300-dimensional embeddings
  • a richer feature set taken from earlier Polish systems, including plWordNet- and Wikipedia-based knowledge features
  • a Siamese architecture
  • more negative training examples
  • global mention clustering

All systems are evaluated on the Polish Coreference Corpus with gold mentions, using MUC, B3, CEAFE and their CoNLL average. Larger embeddings and the Siamese design gave no gains, whereas the extra features clearly helped. The best result came from combining the neural model with a multi-pass sieve of rule-based resolvers, which sets a new state of the art for Polish. The resulting tool is released as Corneferencer.

Key points

  • Data: Polish Coreference Corpus (PCC), built on texts from the National Corpus of Polish and the Rzeczpospolita Corpus. It contains about 1,800 documents in 14 genres, with 540K tokens, 180K mentions and 128K clusters. The authors used a genre-balanced 90/10 train/test split.
  • Baseline network:
    • 1,147 input features feeding three ReLU hidden layers (500/300/100 units) and a sigmoid output
    • trained with Adam for 2 epochs, mini-batch 128, batch normalization and dropout 0.2
    • implemented in Keras on TensorFlow
  • Training used 426K mention pairs, balanced between positive and negative. The baseline classified about 72.27% of 40K test pairs correctly.
  • The best configuration (neural model plus sieve-based resolvers) reaches 81.23 CoNLL F1, which is 0.53 points above the previous Polish state of the art.
  • The models and the Corneferencer tool are publicly available at zil.ipipan.waw.pl/Corneferencer.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages

Topics: Coreference resolution

Cited by

5 papers of our library cite it (of the 140673 whose bibliographies we read); 2 of them have a page here, 3 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work.

@inproceedings{niton2018deep,
  title = {{Deep Neural Networks for Coreference Resolution for Polish}},
  author = {Nitoń, Bartłomiej and Morawiecki, Paweł and Ogrodniczuk, Maciej},
  booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},
  year = {2018},
  doi = {10.63317/3zspjtf5bc5p},
  url = {https://doi.org/10.63317/3zspjtf5bc5p}
}

References

19 references, 7 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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  11. Walter De Gruyter. Ogrodniczuk, M. (2013). Translation- and Projection-Based Unsupervised Coreference Resolution for Polish. In Mieczysław A. Kłopotek, et al., editors, Proceedings of the 20 th International Conference Intelligent Information Systems, volume 7912 of Lecture Notes in Computer Science… find
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