SCIENCE@home

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

Sosuke Kobayashi,Naoaki Okazaki,Kentaro Inui

Published 2017 in International Joint Conference on Natural Language Processing

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.

PUBLICATION RECORD

  • Publication year

    2017

  • Venue

    International Joint Conference on Natural Language Processing

  • Publication date

    2017-09-06

  • Fields of study

    Linguistics, Computer Science

  • Identifiers
    arXiv 1709.01679
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

CITATION MAP

EXTRACTION MAP

CLAIMS

  • No claims are published for this paper.

CONCEPTS

  • No concepts are published for this paper.

REFERENCES

Showing 1-44 of 44 references · Page 1 of 1

CITED BY

Showing 1-14 of 14 citing papers · Page 1 of 1