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Machine Translation of Spanish Personal and Possessive Pronouns Using Anaphora Probabilities

Luong, Ngoc Quang; Popescu-Belis, Andrei; Rios Gonzales, Annette; Tuggener, Don (2017). Machine Translation of Spanish Personal and Possessive Pronouns Using Anaphora Probabilities. In: 15th Conference of the European Chapter of the Association for Computational Linguistics, Valencia, Spain, 5 April 2017 - 7 April 2017. Association for Computational Linguistics, 631-636.

Abstract

We implement a fully probabilistic model to combine the hypotheses of a Spanish anaphora resolution system with those of a Spanish-English machine translation system. The probabilities over antecedents are converted into probabilities for the features of translated pronouns, and are integrated with phrase-based MT using an additional translation model for pronouns.
The system improves the translation of several Spanish personal and possessive pronouns into English, by solving translation divergencies such as ella→she|it or su→his|her|its|their. On a test set with 2,286 pronouns, a baseline system correctly translates 1,055 of them, while ours improves this by 41. Moreover, with oracle antecedents, possessives are translated with an accuracy of 83%.

Additional indexing

Item Type:Conference or Workshop Item (Other), not_refereed, original work
Communities & Collections:06 Faculty of Arts > Institute of Computational Linguistics
Dewey Decimal Classification:000 Computer science, knowledge & systems
410 Linguistics
Scopus Subject Areas:Social Sciences & Humanities > Linguistics and Language
Social Sciences & Humanities > Language and Linguistics
Language:English
Event End Date:7 April 2017
Deposited On:06 Apr 2017 12:53
Last Modified:07 Apr 2022 07:02
Publisher:Association for Computational Linguistics
Funders:Swiss National Science Foundation (SNSF), Sinergia MODERN project (grant n. 147653), European Union Horizon 2020 SUMMA project (grant. n. 88139)
OA Status:Hybrid
Free access at:Official URL. An embargo period may apply.
Publisher DOI:https://doi.org/10.18653/v1/e17-2100
Official URL:http://aclweb.org/anthology/E/E17/E17-2100.pdf
Project Information:
  • Funder: SNSF
  • Grant ID:
  • Project Title: Swiss National Science Foundation (SNSF), Sinergia MODERN project (grant n. 147653)
  • Funder: H2020
  • Grant ID:
  • Project Title: European Union Horizon 2020 SUMMA project (grant. n. 88139)

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