Moses-support Digest, Vol 148, Issue 2

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Today's Topics:

1. CfP: Shared Task: Parallel Corpus Filtering for Low-Resource
Conditions (Paco Guzman)


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Message: 1
Date: Wed, 13 Feb 2019 02:12:39 +0000
From: Paco Guzman <fguzman@fb.com>
Subject: [Moses-support] CfP: Shared Task: Parallel Corpus Filtering
for Low-Resource Conditions
To: "moses-support@mit.edu" <moses-support@mit.edu>
Message-ID: <86F65353-FB49-4350-A076-54219DB545C1@fb.com>
Content-Type: text/plain; charset="utf-8"

[Apologies for cross-posting]

CALL FOR PARTICIPATION
Shared Task: Parallel Corpus Filtering for Low-Resource Conditions
at the Fourth Conference on Machine Translation (WMT19)
http://statmt.org/wmt19/parallel-corpus-filtering.html

This new shared task tackles the problem of cleaning noisy parallel corpora. Following the WMT18 shared task on parallel corpus filtering<http://www.statmt.org/wmt18/parallel-corpus-filtering.html>, we now pose the problem under more challenging low-resource conditions. Instead of German-English, this year there are two low-resource language pairs: Nepali-English and Sinhala-English.
Otherwise, the shared task follows the same set-up: given a noisy parallel corpus (crawled from the web), participants develop methods to filter it to a smaller size of high quality sentence pairs.

DETAILS
We provide a very noisy 35.5 million-word (English token count) Nepali-English corpus and a 59.6 million-word Sinhala-English corpus crawled from the web as part of the Paracrawl<http://paracrawl.eu/> project. We ask participants to provide scores for each sentence in each of the noisy parallel sets. The scores will be used to subsample sentence pairs that amount to 5 million English words. The quality of the resulting subsets is determined by the quality of a statistical machine translation (Moses, phrase-based) and neural machine translation system (FAIRseq) trained on this data. The quality of the machine translation system is measured by BLEU score (sacrebleu) on a held-out test set of Wikipedia translations <https://github.com/facebookresearch/flores> for Sinhala-English and Nepali-English.

We also provide links to training data for the two language pairs. This existing data comes from a variety of sources and is of mixed quality and relevance. We provide a script to fetch and compose the training data.

Note that the task addresses the challenge of data quality and not domain-relatedness of the data for a particular use case. While we provide a development and development test set that are also drawn from Wikipedia articles, these may be very different from the final official test set in terms of topics.
The provided raw parallel corpora are the outcome of a processing pipeline that aimed from high recall at the cost of precision, so they are very noisy. They exhibit noise of all kinds (wrong language in source and target, sentence pairs that are not translations of each other, bad language, incomplete of bad translations, etc.).


IMPORTANT DATES
Release of raw parallel data: February 8, 2019
Submission deadline for subsampled sets: May 10, 2019
System descriptions due: May 17, 2019
Announcement of results: June 3, 2019
Paper notification: June 7, 2019
Camera-ready for system descriptions: June 17, 2019


ORGANIZERS
Philipp Koehn (Johns Hopkins University / University of Edinburgh)
Francisco (Paco) Guzm?n (Facebook)
Vishrav Chaudhary (Facebook)
Juan Pino (Facebook)

More information is available at http://statmt.org/wmt19/parallel-corpus-filtering.html

Similarly to other WMT tasks, intending participants are encouraged to register to https://groups.google.com/forum/#!forum/wmt-tasks for discussions and announcements.


-- Francisco (Paco) Guzman
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