Title: A Neural Language Model for Dynamically Representing the Meanings of Unknown Words and Entities in a Discourse,
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.