Learning to Select, Track, and Generate for Data-to-Text

@inproceedings{Iso2019LearningTS,
  title={Learning to Select, Track, and Generate for Data-to-Text},
  author={Hayate Iso and Yui Uehara and Tatsuya Ishigaki and Hiroshi Noji and Eiji Aramaki and Ichiro Kobayashi and Yusuke Miyao and Naoaki Okazaki and Hiroya Takamura},
  booktitle={ACL},
  year={2019}
}
  • Hayate Iso, Yui Uehara, +6 authors Hiroya Takamura
  • Published in ACL 2019
  • Computer Science
  • We propose a data-to-text generation model with two modules, one for tracking and the other for text generation. Our tracking module selects and keeps track of salient information and memorizes which record has been mentioned. Our generation module generates a summary conditioned on the state of tracking module. Our model is considered to simulate the human-like writing process that gradually selects the information by determining the intermediate variables while writing the summary. In… CONTINUE READING

    Create an AI-powered research feed to stay up to date with new papers like this posted to ArXiv

    Figures, Tables, and Topics from this paper.

    Citations

    Publications citing this paper.

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 37 REFERENCES

    Challenges in Data-to-Document Generation

    VIEW 10 EXCERPTS
    HIGHLY INFLUENTIAL

    Neural Text Generation in Stories Using Entity Representations as Context

    • Elizabeth Clark, Yangfeng Ji, Noah A Smith.
    • Proceedings of the 16th Conference of the North American Chapter of the Association for Computational Linguistics: Human
    • 2018
    VIEW 1 EXCERPT

    DyNet: The Dynamic Neural Network Toolkit