WMT 2016 - First Conference on Machine Translation (WMT)
Date2016-08-07 - 2016-08-12
Deadline2016-02-29
VenueBerlin, Germany
Keywords
Websitehttps://www.statmt.org/wmt16
Topics/Call fo Papers
This year's conference will feature ten shared tasks:
a news translation task,
an IT domain translation task (NEW),
a biomedical translation task (NEW),
an automatic post-editing task,
a metrics task (assess MT quality given reference translation).
a quality estimation task (assess MT quality without access to any reference),
a tuning task (optimize a given MT system),
a pronoun translation task,
a bilingual document alignment task (NEW),
a multimodal translation task (NEW)
In addition to the shared tasks, the conference will also feature scientific papers on topics related to MT. Topics of interest include, but are not limited to:
word-based, phrase-based, syntax-based, semantics-based SMT
neural machine translation
using comparable corpora for SMT
incorporating linguistic information into SMT
decoding
system combination
error analysis
manual and automatic method for evaluating MT
scaling MT to very large data sets
We encourage authors to evaluate their approaches to the above topics using the common data sets created for the shared tasks.
a news translation task,
an IT domain translation task (NEW),
a biomedical translation task (NEW),
an automatic post-editing task,
a metrics task (assess MT quality given reference translation).
a quality estimation task (assess MT quality without access to any reference),
a tuning task (optimize a given MT system),
a pronoun translation task,
a bilingual document alignment task (NEW),
a multimodal translation task (NEW)
In addition to the shared tasks, the conference will also feature scientific papers on topics related to MT. Topics of interest include, but are not limited to:
word-based, phrase-based, syntax-based, semantics-based SMT
neural machine translation
using comparable corpora for SMT
incorporating linguistic information into SMT
decoding
system combination
error analysis
manual and automatic method for evaluating MT
scaling MT to very large data sets
We encourage authors to evaluate their approaches to the above topics using the common data sets created for the shared tasks.
Other CFPs
- 10th SIGHUM Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities
- 7th Workshop on Cognitive Aspects of Computational Language Learning
- 5th Joint Conference on Lexical and Computational Semantics
- 1st Workshop on Representation Learning for NLP
- 13th Learning and Technology International Conference
Last modified: 2016-01-01 09:06:58