Context-sensitive Retrieval for Example-based Translation

Ralf Brown


Abstract
Example-Based Machine Translation (EBMT) systems have typically operated on individual sentences without taking into account prior context. By adding a simple reweighting of retrieved fragments of training examples on the basis of whether the previous translation retrieved any fragments from examples within a small window of the current instance, translation performance is improved. A further improvement is seen by performing a similar reweighting when another fragment of the current input sentence was retrieved from the same training example. Together, a simple, straightforward implementation of these two factors results in an improvement on the order of 1.0–1.6% in the BLEU metric across multiple data sets in multiple languages.
Anthology ID:
2005.mtsummit-ebmt.2
Volume:
Workshop on example-based machine translation
Month:
September 13-15
Year:
2005
Address:
Phuket, Thailand
Venue:
MTSummit
SIG:
Publisher:
Note:
Pages:
9–15
Language:
URL:
https://aclanthology.org/2005.mtsummit-ebmt.2
DOI:
Bibkey:
Cite (ACL):
Ralf Brown. 2005. Context-sensitive Retrieval for Example-based Translation. In Workshop on example-based machine translation, pages 9–15, Phuket, Thailand.
Cite (Informal):
Context-sensitive Retrieval for Example-based Translation (Brown, MTSummit 2005)
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PDF:
https://preview.aclanthology.org/emnlp22-frontmatter/2005.mtsummit-ebmt.2.pdf