Paraphrase thought: Sentence embedding module imitating human language recognition

Myeongjun Jang, Pilsung Kang

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


Sentence embedding is an important research topic in natural language processing. It is essential to generate a good embedding vector that fully reflects the semantic meaning of a sentence in order to achieve an enhanced performance for various natural language processing tasks, such as machine translation and document classification. Thus far, various sentence embedding models have been proposed, and their feasibility has been demonstrated through good performances on tasks following embedding, such as sentiment analysis and sentence classification. However, because the performances of sentence classification and sentiment analysis can be enhanced by using a simple sentence representation method, it is not sufficient to claim that these models fully reflect the meanings of sentences based on good performances for such tasks. In this paper, inspired by human language recognition, we propose the following concept of semantic coherence, which should be satisfied for a good sentence embedding method: similar sentences should be located close to each other in the embedding space. Then, we propose the Paraphrase-Thought (P-thought) model to pursue semantic coherence as much as possible. Experimental results on three paraphrase identification datasets (MS COCO, STS benchmark, SICK) show that the P-thought models outperform the benchmarked sentence embedding methods.

Original languageEnglish
Pages (from-to)123-135
Number of pages13
JournalInformation Sciences
Publication statusPublished - 2020 Dec

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Korea government (MSIT) (No. NRF-2019R1F1A1060338) and Korea Institute for Advancement of Technology (KIAT) grant funded by the Korea Government (MOTIE) (P0008691, The Competency Development Program for Industry Specialist).

Publisher Copyright:
© 2020 Elsevier Inc.


  • Natural language processing
  • Paraphrase
  • Recurrent neural network
  • Semantic coherence
  • Sentence embedding

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence


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