articleJan 1, 2017GOLD OA

SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation

DCDaniel CerMDMona DiabEAEneko AgirreILInigo Lopez-GazpioLSLucia Specia

Google (United States) · George Washington University · +2 more institutions

Indexed inarxivcrossref

Abstract

Semantic Textual Similarity (STS) measures the meaning similarity of sentences. Applications include machine translation (MT), summarization, generation, question answering (QA), short answer grading, semantic search, dialog and conversational systems. The STS shared task is a venue for assessing the current state-of-the-art. The 2017 task focuses on multilingual and cross-lingual pairs with one sub-track exploring MT quality estimation (MTQE) data. The task obtained strong participation from 31 teams, with 17 participating in all language tracks. We summarize performance and review a selection of well performing methods. Analysis highlights common errors, providing insight into the limitations of existing…

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Authors

5
  • DC
    Daniel CerCorresponding

    Google (United States)

  • MD
    Mona Diab

    George Washington University

  • EA
    Eneko Agirre

    University of the Basque Country

  • IL
    Inigo Lopez-Gazpio

    University of the Basque Country

  • LS
    Lucia Specia

    University of Sheffield

Topics & keywords

Keywords
  • Task (project management)
  • Semantic similarity
  • Similarity (geometry)
  • Machine translation
  • Dialog box
  • Meaning (existential)
  • Set (abstract data type)
  • Selection (genetic algorithm)
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