Conceptual Annotations Preserve Structure Across Translations: A French-English Case Study

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Abstract

Divergence of syntactic structures between languages constitutes a major challenge in using linguistic structure in Machine Translation (MT) systems. Here, we examine the potential of semantic structures. While semantic annotation is appealing as a source of cross-linguistically stable structures, little has been accomplished in demonstrating this stability through a detailed corpus study. In this paper, we experiment with the UCCA conceptual-cognitive annotation scheme in an English-French case study. First, we show that UCCA can be used to annotate French, through a systematic type-level analysis of the major French grammatical phenomena. Second, we annotate a parallel English-French corpus with UCCA, and quantify the similarity of the structures on both sides. Results show a high degree of stability across translations, supporting the usage of semantic annotations over syntactic ones in structure-aware MT systems.
Original languageEnglish
Title of host publicationProceedings of the 1st Workshop on Semantics-Driven Statistical Machine Translation (S2MT 2015)
EditorsDeyi Xiong, Kevin Duh, Christian Hardmeier, Roberto Navigli
Place of PublicationBeijing, China
PublisherAssociation for Computational Linguistics (ACL)
Pages11-22
Number of pages12
ISBN (Electronic)978-1-941643-61-7
DOIs
StatePublished - 1 Jul 2015
Event1st Workshop on Semantics-Driven Statistical Machine Translation (S2MT 2015) - Beijing, China
Duration: 30 Jul 201530 Jul 2015
Conference number: 1
https://aclanthology.org/volumes/W15-35/

Workshop

Workshop1st Workshop on Semantics-Driven Statistical Machine Translation (S2MT 2015)
Country/TerritoryChina
CityBeijing
Period30/07/1530/07/15
Internet address

Keywords

  • linguistic structure
  • Machine Translation
  • MT
  • semantic structures
  • UCCA
  • semantic annotations

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