Predicting and Using a Pragmatic Component of Lexical Aspect of Simple Past Verbal Tenses for Improving English-to-French Machine Translation

Sharid Loáiciga, Cristina Grisot


Abstract
This paper proposes a method for improving the results of a statistical Machine Translation system using boundedness, a pragmatic component of the verbal phrase’s lexical aspect. First, the paper presents manual and automatic annotation experiments for lexical aspect in EnglishFrench parallel corpora. It will be shown that this aspectual property is identified and classified with ease both by humans and by automatic systems. Second, Statistical Machine Translation experiments using the boundedness annotations are presented. These experiments show that the information regarding lexical aspect is useful to improve the output of a Machine Translation system in terms of better choices of verbal tenses in the target language, as well as better lexical choices. Ultimately, this work aims at providing a method for the automatic annotation of data with boundedness information and at contributing to Machine Translation by taking into account linguistic data.
Anthology ID:
2016.lilt-13.3
Volume:
Linguistic Issues in Language Technology, Volume 13, 2016
Month:
Year:
2016
Address:
Venue:
LILT
SIG:
Publisher:
CSLI Publications
Note:
Pages:
Language:
URL:
https://aclanthology.org/2016.lilt-13.3
DOI:
Bibkey:
Cite (ACL):
Sharid Loáiciga and Cristina Grisot. 2016. Predicting and Using a Pragmatic Component of Lexical Aspect of Simple Past Verbal Tenses for Improving English-to-French Machine Translation. In Linguistic Issues in Language Technology, Volume 13, 2016. CSLI Publications.
Cite (Informal):
Predicting and Using a Pragmatic Component of Lexical Aspect of Simple Past Verbal Tenses for Improving English-to-French Machine Translation (Loáiciga & Grisot, LILT 2016)
Copy Citation:
PDF:
https://aclanthology.org/2016.lilt-13.3.pdf