The paper presents several configurations of deep neural networks aimed at the task of coreference resolution for Polish. Starting with the basic feature set and standard word embedding vector size we examine the setting with larger vectors, more extensive sets of mention features, increased number of negative examples, Siamese network architecture and a global mention connection algorithm. The highest results are achieved by the system combining our best deep neural architecture with the sieve-based approach – the cascade of rule-based coreference resolvers ordered from most to least precise. All systems are evaluated on the data of the Polish Coreference Corpus featuring 540K tokens and 180K mentions. The best variant improves the state of the art for Polish by 0.53 F1 points, reaching 81.23 points of the CoNLL metric.
@InProceedings{NITOŃ18.183, author = {Bartłomiej Nitoń and Paweł Morawiecki and Maciej Ogrodniczuk}, title = "{Deep Neural Networks for Coreference Resolution for Polish}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
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