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AMTA 2012 Workshop on Monolingual Machine Translation (MONOMT 2012)

Submission Deadline Now Extended to 14th Aug, 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)


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;
  10. Error identification and voting with independent monolingual crowdsources [Hu et al., 11].)

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 14, 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 (See below). 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 (NICT, Japan)
PROGRAM COMMITTEE (Tentative version)
  • Bogdan Babych (University of Leeds, UK)
  • Loic Barrault (LIUM, Universite du Maine, France)
  • Nicola Bertoldi (FBK, Italy)
  • Ergun Bicici (CNGL, Dublin City University, Ireland)
  • 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)