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Addicter stands for Automatic Detection and DIsplay of Common Translation ERrors. It will be a set of tools (mostly scripts written in Perl) that help with error analysis for machine translation.

The work on Addicter has started at the MT Marathon 2010 in Dublin, within a broader 5-day project called Failfinder (Dan Zeman, Ondřej Bojar, Martin Popel, David Mareček, Jon Clark, Ken Heafield, Qin Gao, Loïc Barrault). The code that resulted from the project can be freely downloaded from https://failfinder.googlecode.com/svn/trunk/. The nucleus that existed just after the MT Marathon (4 Feb 2010) is Addicter version 0.1, to reflect that this was by no means deemed a final product.

In 2011, the viewer was accompanied by an automatic error recognizer and classifier, thanks to Mark Fishel. The development has been moved to ÚFAL StatMT SVN repository (i.e. failfinder.googlecode.com is currently not maintained). In September 2011 at the Sixth MT Marathon in Trento, Addicter was further developed and thoroughly compared with another tool for error analysis, Hjerson. See the project wiki. For further developments, see also the Terra website.

Currently, Addicter can do the following:

The viewing and browsing is performed using a web server that generates web pages dynamically (to avoid pre-generating millions of static HTML documents). Words in sentences are clickable so that the user can quickly navigate to examples and summaries of other than the current word. If you have access to a webserver you may use Addicter with it; otherwise you can use Addicter's own lightweight server. A small subset can be also generated as static HTML files and viewed without a web server: the test data browser.

There is another subpage for Addicter in this wiki that lies in the external name space, thus it can be used for external collaboration.


How to install and configure Apache

NOTE: Since September 2011, it is not necessary to install a local web server, so skip this section if you do not want it. Addicter now comes with a script called server.pl that works as a HTTP daemon and serves Addicter content (but nothing else) to your browser. This section is thus optional.

Microsoft Windows

This tutorial currently focuses on installing Apache HTTP Server on Microsoft Windows. If you are experienced user of another operating system and wish to share advice, please feel free to contact me.

Ubuntu Linux

Install the Apache HTTP server package. After successful installation, there should be a file /etc/apache2/sites-enabled/000-default. Edit it (you need root permissions). There should be a section similar to the following:

	ScriptAlias /cgi-bin/ /usr/lib/cgi-bin/
	<Directory "/usr/lib/cgi-bin">
		AllowOverride None
		Options +ExecCGI -MultiViews +SymLinksIfOwnerMatch
		Order allow,deny
		Allow from all

Either create a copy of the section with new alias and path (eg. ScriptAlias /addicter-cgi/ /home/user/addicter/cgi/) or use the /usr/lib/cgi-bin (or whatever folder you see by default) for your addicter CGI scripts and data (see below).

How to install Addicter

We use $CGI to refer to the path you registered with Apache as containing CGI scripts (using the ScriptAlias directive). NOTE: If you are using Addicter's own web server or if Addicter content is the only thing you intend to use the server to serve, probably the easiest thing to do is to set the Addicter's cgi folder as your $CGI. NOTE 2: There are couple of files with static (non-CGI) web content, needed by the CGI scripts. These files (currently tabs.gif and activatables.js) are in $CGI/... With Addicter's own web server, this is just fine. If you are using another web server, however, you must copy these files to the appropriate location in your static content directory structure so that the server finds them. They should not be directly in the $CGI folder because they are not scripts and should not be treated as scripts by the server.

Alignment viewer

Before invoking the viewer, you need to run an indexing script over your aligned corpus. It will create a bunch of index files that will later tell the viewer where to look for examples of a particular word. The indexer needs the following input files:

<!–The prepare folder contains some sample corpora in sample_data.zip.–>

The indexer splits the output index into multiple files in order to reduce size of any individual file. All index files must be stored in the experiment subfolder of $CGI so that the CGI scripts can find them.

How to prepare a corpus for viewing

We assume that your corpus is already sentence-aligned and tokenized. I.e., source and target files have the same number of lines (sentences, segments), and tokens (words, punctuation) on each line are space-separated. If you are using Addicter to perform analysis of errors made by a machine translation system, you probably already have such a corpus. You may also want to use a lowercased version of your corpus. Unless stated otherwise, all files are supposed to be plain text files in the UTF-8 encoding.

You will also need some alignment files that define bi-directional word alignments. If you have trained a statistical MT system such as Moses, chances are that you already have such files for the training data. They result from the first three steps of the Moses training pipeline, namely from two runs of Giza++ and an alignment symmetrization algorithm. In order to get alignments for test data, too, you can do the following:

Once all the input files are ready, the indexer is invoked as follows:

addictindex.pl \
    -trs train.en -trt train.hi -tra train.ali \
    -s test.en -r test.hi -h test.system.hi -ra test.ali -ha test.system.ali \
    -o $CGI

The indexer will copy the input files and output all index files into the $CGI folder where the CGI scripts will find them.

How to invoke the error classifier

The error classifier currently uses its own monlingual word-alignment of reference translation and the hypothesis. It is invoked as follows:

${ADDICTER}/prepare/detecter.pl -s srcfile -r reffile -h hypfile [-a alignment] -w workdir

and it creates the files workdir/tcali.txt and workdir/tcerr.txt. The input files (src, ref and hyp) can also be gzipped. Custom alignment between hypothesis and reference can be supplied. If it is not supplied, then the default aligner (${ADDICTER}/testchamber/align-greedy.pl) is invoked.

Place the files tcali.txt and tcerr.txt in the experiment subfolder of $CGI and the error classes will be displayed during test data browsing in the viewer. The viewer can work with several alternating alignments (perhaps using different aligning algorithms) of the same data. For each of those alignments, you have to run detecter.pl separately.

How to use the viewer

First make sure that your web server is running and configured properly and that your index and data files have been prepared in the correct place. If you do not use your own web server, invoke the script server.pl in the main Addicter folder. It will say something like

Please contact me at: <URL:http://localhost:2588/cgi/index.pl>

which is the URL you should point your browser to. The server uses a randomly picked port number unless you specify it as a commandline parameter: server.pl 8080.

In the browser, you will see a list of experiments (all subfolders of $CGI). Start browsing your data by clicking on an experiment.


This research has been supported by the grant of the Czech Ministry of Education no. MSM0021620838 (2010), by the grants of the Czech Science Foundation no. P406/11/1499 and P406/10/P259, the Estonian Science Foundation target financed theme SF0180078s08 (2011) and by the project EuroMatrixPlus (FP7-ICT-2007-3-231720 of the EU and 7E09003+7E11051 of the Ministry of Education, Youth and Sports of the Czech Republic; 2011-2012).


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