@article{MEC52AFB3,
title = "Coreference Resolution using Multi-resolution Pointer Networks",
journal = "Journal of KIISE, JOK",
year = "2019",
issn = "2383-630X",
doi = "10.5626/JOK.2019.46.4.334",
author = "Cheoneum Park,Changki Lee,Hyunki Kim",
keywords = "coreference resolution,pointer networks,attention mechanism",
abstract = "Multi-resolution RNN is a method of modeling parallel sequences as RNNs. Coreference resolution is a natural language processing task in which several words representing different entities present in a document are defined as one cluster and can be solved by a pointer network. The encoder input sequence of the coreference resolution becomes all the morphemes of the document using the pointer network, and the decoder input sequence becomes all the nouns present in the document. In this paper, we propose three multi-resolution pointer network models that encode all morphemes and noun lists of a document in parallel and perform decoding by using both encoded hidden states in a decoder. We have solved the coreference resolution based on the proposed models. Experimental results show that Multi-resolution1 of the proposed model has 71.44% CoNLL F1, 70.52% CoNLL F1 of Multi-resolution2 and 70.59% CoNLL F1 of Multi-resolution3."
}