[Corpora-List] Call For Participation: MONOMT 2012 (AMTA)

Tue Sep 18 13:41:08 UTC 2012

** apologies for multiple postings **
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Call for participation
Early registration deadline:  Thursday, **September 20**.
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**
AMTA 2012 Workshop on Monolingual Machine Translation (MONOMT 2012)
Date: Nov 1, 2012
Location: San Diego, United States
*** Colocated with AMTA 2012 (The Tenth Biennial Conference of the
Association for
Machine Translation in the America)

http://computing.dcu.ie/~tokita/MONOMT/monomt.htm**

*Invited Speakers*
*
*
Marcello Federico (FBK, Italy)
George Foster (National Research Council Canada, Canada)
Philipp Koehn (university of Edinburgh, UK)
Qun Liu (Dublin City University, Ireland & Chinese Academy of Science,
China)
Evgeny Matusov (SAIC)
Taro Watanabe (NICT, Japan)
(More ...)

*
ACCEPTED PAPERS
*

Long Presentations
*

    - Improving English to Spanish Out-of-Domain Translations by Morphology
   Generalization and Generation
   Lluis Formiga, Adolfo Hernandez, Jose B. Marino, and Enric Monte
   - Monolingual Data Optimisation for Bootstrapping SMT Engines
   Jie Jiang, Andy Way, Nelson Ng, Rejwanul Haque, Mike Dillinger, and Jun
   Lu
   - Shallow and Deep Paraphrasing for Improved Machine Translation
   Parameter Optimization
   Dennis Nolan Mehay and Michael White

Short Presentations

   - Two stage Machine Translation System using Pattern-based MT and
   Phrase-based SMT
   Jin'ichi Murakami, Takuya Nishimura and Masato Tokuhisa
   - Improving Word Alignment by Exploiting Adapted Word Similarity
   Septina Dian Larasati
   - Addressing some Issues of Data Sparsity towards Improving
   English-Manipuri SMT using Morphological Information
   Thoudam Doren Singh
   - Statistical Machine Translation for Depassivizing German
   Part-of-speech Sequences
   Benjamin Gottesman

   DESCRIPTION

   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; Simard 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.

   IMPORTANT DATES

   Submission deadline: August 14, 2012
   Notification to authors: August 31, 2012
   Camera ready: September 7, 2012
   Workshop: November 1, 2012

   TOPICS OF INTEREST

   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-Translation Memory integration
       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)

   WORKSHOP CHAIRS

   Tsuyoshi Okita (DCU, Ireland)
   Artem Sokolov (LIMSI, France)
   Taro Watanabe (NICT, Japan)

   PROGRAM COMMITTEE (Tentative)

   Bogdan Babych (University of Leeds, UK)
   Loic Barrault (LIUM, Universite du Maine, France)
   Nicola Bertoldi (FBK, Italy)
   Ergun Bicici (CNGL, Dublin City University, Ireland)
   Ondrej Bojar (Charles University, Czech)
   Boxing Chen (NRC Institute for Information Technology, Canada)
   Trevor Cohn (University of Sheffield, UK)
   Marta Ruiz Costa-jussa (Barcelona Media, Spain)
   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)
   Christian Federmann (DFKI, Germany)
   Yvette Graham (Dublin City University, Ireland)
   Barry Haddow (University of Edinburgh, UK)
   Xiadong He (Microsoft, USA)
   Jagadeesh Jagarlamudi (University of Maryland, USA)
   Jie Jiang (Applied Language Solutions, UK)
   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)
   Maite Melero (Barcelona Media, Spain)
   Philip Resnik (University of Maryland, USA)
   Stefan Riezler (University of Heidelberg, Germany)
   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, Hong Kong)
   Francois Yvon (LIMSI, University Paris Sud, France)

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