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Subject: [ E-CFP ] AMTA 2012 Workshop on Monolingual Machine Translation (MONOMT 2012) -- CFP
From: <rasmusse_(on)_ptd.net>
Date received: 22 Jun 2012
Deadline: 03 Aug 2012
Start date: 01 Nov 2012

AMTA 2012 Workshop on Monolingual Machine Translation (MONOMT
2012) Title: Monolingual Machine Translation (MONOMT 2012).

Date: Nov 1, 2012 Location: San Diego, United States


Due to the increasing demands for high quality translation,
monolingual Machine Translation (MT) subtasks are frequently
encountered in various occasions, where one MT task is decomposed
into several subtasks some of which can be called `monolingual'.
Such monolingual MT subtasks include: (1) MT for morphologically
rich languages, [Bojar, 08] aimed at dealing with morphologic
richness of the target, as is the case with the English-Czech
(EN-CZ) language pair. An MT task is thus split into two
subtasks: first, English is (`bilingually') translated into
simplified Czech and then, the obtained morphologically
normalized Czech is (`monolingually') translated into
morphologically rich Czech; (2) system combination [Matusov et
al., 05], where a source sentence is first translated into the
target language by several MT systems, and then, the obtained
translations are combined to create / generate the output in the
same language; (3) statistical post-editing [Dugast et al., 07;
Simar d et al., 07], where a source sentence is first translated
into the target language by a rule-based MT system and then, the
obtained output is `monolingually' translated by an SMT system;
(4) domain adaptation using transfer learning [Daume III, 07]:
the source side written in a `source' domain (e.g., newswires) is
converted into the target side written in a `target' domain
(e.g., patents); (5) transliteration between phonemes / alphabets
[Knight and Graehl, 98]; (6) considering reordering issues (SVO
and SOV) [Katz-Brown et al., 11]; (7) MERT process [Arun et al.,
10]; (8) translation memory (TM) and MT integration [Ma et al.,
11]; (9) paraphrasing for creating additional training data or
for evaluation purposes.

A distinction could be established between bilingual MT tools
(B-tools) and monolingual MT tools (M-tools) that may be
exploited for monolingual MT. Consider, e.g., monolingual
subtasks such as MT for morphologically rich languages,
statistical post-editing, or transliteration and a task of system
combination or domain adaptation as respective representatives.
The latter group is often approached with monolingual M-tools
like monolingual word alignment [Matusov et al., 05; He et al.,
08] and the minimization of Bayes risk [Kumar and Byrne, 02] (on
the outputs of combined systems). However, the former usually
employs bilingual MT tools, like GIZA++ [Och and Ney, 04] to
extract bilingual phrases and MAP decoding on them. The way
M-tools and B-tools are used for monolingual MT is an issue of
particular interest for this workshop.

This workshop is intended to provide the opportunity to discuss
ideas and share opinions on the question of the applicability of
M-tools or B-tools for monolingual MT subtasks, and on their
respective strengths and weaknesses in specific settings.
Furthermore we wish to provide opportunity to demonstrate
successful usecases of M-tools.

Possible questions, that are encouraged to be addressed during
the workshop, include:

ways of applying M-tools to monolingual MT subtasks such as MT
for morphologically rich languages and statistical post-editing.
investigation of the suitability of B-tools or M-tools for
monolingual MT subtasks. performance improvements of monolingual
word alignment tools, since these are necessary for specific
monolingual subtasks, such as MT for morphologically rich
languages and statistical post-editing.


Submission deadline: August 3, 2012

Notification to authors: August 31, 2012 Camera ready: September
7, 2012

Workshop: November 1, 2012


Original papers are invited on different aspects of monolingual
MT, such as:

MT for morphologically rich languages system combination
statistical post-editing domain adaptation MERT process MT for
reordering mismatched language pairs (SVO and SOV, ...) MT-TM
integration (i.e. MT systems whose prior knowledge includes
bilingual terminology and TM) transliteration MT using textual
entailment MT using confidence estimation paraphrasing hybrid MT
... Papers describing the mechanism of MT tools that may be
considered `monolingual' are also encouraged. Some possible
topics are listed below:

MBR decoding, consensus decoding monolingual word alignment
(based on TER, METEOR,...) language models constructed by
learning the representation of data data structure related
matters ranking algorithms multitask learning (in the context of
domain adaptation) ...


Authors are invited to submit long papers (up to 10 pages) and
short papers (2 - 4 pages). Long papers should describe
unpublished, substantial and completed research. Short papers
should be position papers, papers describing work in progress or
short, focused contributions. Papers will be accepted until
August 3, 2012 in PDF format via the system. Submitted papers
must follow the styles and formatting guidelines available from
the AMTA main conference site. As the reviewing will be blind,
the papers must not include the authors' names and affiliations.
Furthermore, self-references that reveal the author's identity,
e.g., "We previously showed (Smith, 1991) ..." must be avoided.
Instead, use citations such as "Smith previously showed (Smith,
1991) ..." Papers that do not conform to these requirements will
be rejected without review.


Tsuyoshi Okita (Dublin City University, Ireland) Artem Sokolov
(LIMSI, France) Taro Watanabe (National Institute of Information
and Communications Technology, Japan)


Bogdan Babych (University of Leeds, UK) Loic Barrault (LIUM,
Universite du Maine, France) Nicola Bertoldi (FBK, Italy) Boxing
Chen (NRC Institute for Information Technology, Canada) Trevor
Cohn (University of Sheffield, UK) Josep M. Crego (SYSTRAN,
France) John DeNero (Google, USA) Jinhua Du (Xi'an University of
Technology, China) Kevin Duh (Nara Institute of Science and
Technology, Japan) Chris Dyer (CMU, USA) Barry Haddow (University
of Edinburgh, UK) Xiadong He (Microsoft, USA) Jagadeesh
Jagarlamudi (University of Maryland, USA) Philipp Koehn
(University of Edinburgh, UK) Shankar Kumar (Google, USA) Alon
Lavie (CMU, USA) Yanjun Ma (Baidu, China) Aurelien Max (LIMSI,
University Paris Sud, France) Lucia Specia (University of
Sheffield, UK) Marco Turchi (JRC, Italy) Antal van den Bosch
(Radboud University Nijmegen, Netherlands) Xianchao Wu (Baidu,
Japan) Dekai Wu (HKUST, Honkong) Francois Yvon (LIMSI, University
Paris Sud, France)

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