Header

UZH-Logo

Maintenance Infos

Pre-reordering for Statistical Machine Translation of Non-fictional Subtitles


Plamada, Magdalena; Linder, Gion; Ströbel, Phillip; Volk, Martin (2015). Pre-reordering for Statistical Machine Translation of Non-fictional Subtitles. In: The 18th Annual Conference of the European Association for Machine Translation (EAMT 2015), Antalya, Turkey, May 2015 - May 2015.

Abstract

This paper describes the challenges of building a Statistical Machine Translation (SMT) system for non-fictional subtitles. Since our experiments focus on a "difficult" translation direction (i.e. French-German), we investigate several methods to improve the translation performance. We also compare our in-house SMT systems (including domain adaptation and pre-reordering techniques) to other SMT services and show that pre-reordering alone significantly improves the baseline systems.

Abstract

This paper describes the challenges of building a Statistical Machine Translation (SMT) system for non-fictional subtitles. Since our experiments focus on a "difficult" translation direction (i.e. French-German), we investigate several methods to improve the translation performance. We also compare our in-house SMT systems (including domain adaptation and pre-reordering techniques) to other SMT services and show that pre-reordering alone significantly improves the baseline systems.

Statistics

Downloads

43 downloads since deposited on 05 Aug 2015
17 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:06 Faculty of Arts > Institute of Computational Linguistics
Dewey Decimal Classification:000 Computer science, knowledge & systems
410 Linguistics
Language:English
Event End Date:May 2015
Deposited On:05 Aug 2015 14:41
Last Modified:17 Aug 2017 04:15
Publisher:s.n.
Free access at:Official URL. An embargo period may apply.
Official URL:http://www.eamt2015.org/files/downloads/EAMT2015_Proceedings.pdf

Download

Preview Icon on Download
Preview
Content: Presentation
Filetype: PDF
Size: 120kB
Licence: Creative Commons: Attribution-No Derivatives 4.0 International (CC BY-ND 4.0)

TrendTerms

TrendTerms displays relevant terms of the abstract of this publication and related documents on a map. The terms and their relations were extracted from ZORA using word statistics. Their timelines are taken from ZORA as well. The bubble size of a term is proportional to the number of documents where the term occurs. Red, orange, yellow and green colors are used for terms that occur in the current document; red indicates high interlinkedness of a term with other terms, orange, yellow and green decreasing interlinkedness. Blue is used for terms that have a relation with the terms in this document, but occur in other documents.
You can navigate and zoom the map. Mouse-hovering a term displays its timeline, clicking it yields the associated documents.

Author Collaborations