@InProceedings{kobayashi-okazaki-inui:2017:I17-1,
  author    = {Kobayashi, Sosuke  and  Okazaki, Naoaki  and  Inui, Kentaro},
  title     = {A Neural Language Model for Dynamically Representing the Meanings of Unknown Words and Entities in a Discourse},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {473--483},
  abstract  = {This study addresses the problem of identifying the meaning of unknown words or
	entities in a discourse with respect to the word embedding approaches used in
	neural language models. We proposed a method for on-the-fly construction and
	exploitation of word embeddings in both the input and output layers of a neural
	model by tracking contexts. This extends the dynamic entity representation used
	in Kobayashi et al. (2016) and incorporates a copy mechanism proposed
	independently by Gu et al. (2016) and Gulcehre et al. (2016). In addition, we
	construct a new task and dataset called Anonymized Language Modeling for
	evaluating the ability to capture word meanings while reading. Experiments
	conducted using our novel dataset show that the proposed variant of RNN
	language model outperformed the baseline model. Furthermore, the experiments
	also demonstrate that dynamic updates of an output layer help a model predict
	reappearing entities, whereas those of an input layer are effective to predict
	words following reappearing entities.},
  url       = {http://www.aclweb.org/anthology/I17-1048}
}