ACL 2010 FIFTH WORKSHOP
ON STATISTICAL MACHINE TRANSLATION

Shared Task: Machine Translation for European Languages

July 15-16, in conjunction with ACL 2010 in Uppsala, Sweden

[HOME] | [TRANSLATION TASK] | [SYSTEM COMBINATION TASK] | [EVALUATION TASK] | [RESULTS]
[BASELINE SYSTEM] | [BASELINE SYSTEM 2]
[SCHEDULE] | [PAPERS] | [AUTHORS]

The translation task of this workshop focuses on European language pairs. Translation quality will be evaluated on a shared, unseen test set of news stories. We provide a parallel corpus as training data, a baseline system, and additional resources for download. Participants may augment the baseline system or use their own system.

Goals

The goals of the shared translation task are: We hope that both beginners and established research groups will participate in this task.

Changes This Year

After a number of extensions over the last years, this year's translation task will be more focused. The motivation for this is to have a clearly defined task and to be able to gather sufficient human judgement data to make as many statistically significant distinctions as possible.

  • Two Official Manual Metrics: Two manual evaluation metrics will be used to rank translations: human preference judgments on a sentence-by-sentence basis, and human sentence editing and correctness assessment (see last year's workshop for details).

  • Manual Evaluation with Mechanic Turk: In addition to manual evaluation carried out by volunteers and participants, manual evaluation will also be done with Amazon's Mechanical Turk. We will compare the assessments from the two types of evaluators.

  • More Training Data: The parallel corpora Europarl and News Commentary and the monolingual News corpora are extended, in addition to the large French-English corpus which was already released year. We release large French-English and Spanish-English parallel corpora crawled from United Nation sources by DFKI. We include the relevant LDC Gigaword corpora in the constraint data condition.

    Task Description

    We provide training data for four European language pairs, and a common framework (including a baseline system). The task is to improve methods current methods. This can be done in many ways. For instance participants could try to Participants will use their systems to translate a test set of unseen sentences in the source language. The translation quality is measured by a manual evaluation and various automatic evaluation metrics. Participants agree to contribute to the manual evaluation about eight hours of work.

    You may participate in any or all of the following language pairs:

    For all language pairs we will test translation in both directions. To have a common framework that allows for comparable results, and also to lower the barrier to entry, we provide a common training set and baseline system.

    We also strongly encourage your participation, if you use your own training corpus, your own sentence alignment, your own language model, or your own decoder.

    If you use additional training data or existing translation systems, you must flag that your system uses additional data. We will distinguish system submissions that used the provided training data (constrained) from submissions that used significant additional data resources. Note that basic linguistic tools such as taggers, parsers, or morphological analyzers are allowed in the constrained condition.

    Your submission report should highlight in which ways your own methods and data differ from the standard task. We may break down submitted results in different tracks, based on what resources were used. We are mostly interested in submission that are constraint to the provided training data, so that the comparison is focused on the methods, not on the data used. You may submit contrastive runs to demonstrate the benefit of additional training data.

    Training Data

    The provided data is mainly taken from a new release (version 5) of the Europarl corpus, which is freely available. Please click on the links below to download the sentence-aligned data, or go to the Europarl website for the source release.

    Additional training data is taken from the new News Commentary corpus. There are about 45 million words of training data per language from the Europarl corpus and 2 million words from the News Commentary corpus.

    Europarl
    • French-English
    • Spanish-English
    • German-English
    • French monolingual
    • Spanish monolingual
    • German monolingual
    • English monolingual
    News Commentary
    • French-English
    • Spanish-English
    • German-English
    • Czech-English
    • French monolingual
    • Spanish monolingual
    • German monolingual
    • Czech monolingual
    • English monolingual
    News
    • French monolingual
    • Spanish monolingual
    • German monolingual
    • English monolingual
    • Czech monolingual
    United Nations
    • French-English
    • Spanish-English
    French-English 109 corpus
    • French-English
    Crawled from Canadian and European Union sources.
    CzEng
    • Czech-English
    The current version of the CzEng corpus (version v0.9) is available from the CzEng web site.

    You may also use the following monolingual corpora released by the LDC:

    Note that the released data is not tokenized and includes sentences of any length (including empty sentences). All data is in Unicode (UTF-8) format. The following tools allow the processing of the training data into tokenized format:

    Development Data

    To tune your system during development, we suggest using the 2008 test set of 2051 sentences. The data is provided in raw text format and in an SGML format that suits the NIST scoring tool. We also release the 2009 test set of 2525 sentences and a system combination tuning set of 502 sentences.

    News news-test2008
    • English
    • French
    • Spanish
    • German
    • Czech
    • Hungarian
    This data is a cleaned version of the 2008 test set.
    News news-test2009
    • English
    • French
    • Spanish
    • German
    • Czech
    • Hungarian
    • Italian
    News news-syscomb2009
    • English
    • French
    • Spanish
    • German
    • Czech
    • Hungarian
    • Italian

    Test Data

    The test set is similar in nature as the prior test sets.

    News news-test2010
    • English
    • French
    • Spanish
    • German
    • Czech

    Download

    Test Set Submission

    To submit your results, please first convert into into SGML format as required by the NIST BLEU scorer, and then upload it to the website
    matrix.statmt.org.

    SGML Format

    Each submitted file has to be in a format that is used by standard scoring scripts such as NIST BLEU or TER.

    This format is similar to the one used in the source test set files that were released, except for:

    The script wrap-xml.perl makes the conversion of a output file in one-segment-per-line format into the required SGML file very easy:

    Format: wrap-xml.perl LANGUAGE SRC_SGML_FILE SYSTEM_NAME < IN > OUT
    Example: wrap-xml.perl en newstest2010-src.de.sgm Google < decoder-output > decoder-output.sgm

    Upload to Website

    Upload happens in three easy steps: If you are submitting contrastive runs, please submit your primary system first and mark it clearly as the primary submission.

    Evaluation

    Evaluation will be done both automatically as well as by human judgement.

    Dates

    Results

    The results of the evaluation are reported in the workshop overview paper.

    supported by the EuroMatrixPlus project
    P7-IST-231720-STP
    funded by the European Commission
    under Framework Programme 7