Vector-to-sequence models for sentence analogies

Liyan Wang, Yves Lepage

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We solve sentence analogies by generating the solution rather than identifying the best candidate from a given set of candidates, as usually done. We design a decoder to transform sentence embedding vectors back into sequences of words. To generate the vector representations of answer sentences, we build a linear regression network which learns the mapping between the distribution of known and expected vectors. We subsequently leverage this pre-trained decoder to decode sentences from regressed vectors. The results of experiments conducted on a set of semantico-formal sentence analogies show that our proposed solution performs better than a state-of-the-art baseline vector offset method which solves analogies using embeddings.

Original languageEnglish
Title of host publication2020 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages441-446
Number of pages6
ISBN (Electronic)9781728192796
DOIs
Publication statusPublished - 2020 Oct 17
Event12th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2020 - Virtual, Depok, Indonesia
Duration: 2020 Oct 172020 Oct 18

Publication series

Name2020 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2020

Conference

Conference12th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2020
Country/TerritoryIndonesia
CityVirtual, Depok
Period20/10/1720/10/18

Keywords

  • Decoder
  • Sentence analogies
  • Sentence embeddings

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Information Systems
  • Signal Processing

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