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Document-Level Local Search Decoder for Phrase-Based Statistical Machine Translation
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README

DOCENT -- DOCUMENT-LEVEL LOCAL SEARCH DECODER FOR PHRASE-BASED SMT
==================================================================
Christian Hardmeier
26 September 2012

Sebastian Schleussner
13 July 2016

Docent is a decoder for phrase-based Statistical Machine Translation (SMT).
Unlike most existing SMT decoders, it treats complete documents, rather than
single sentences, as translation units and permits the inclusion of features
with cross-sentence dependencies to facilitate the development of
discourse-level models for SMT. Docent implements the local search decoding
approach described by Hardmeier et al. (EMNLP 2012).

If you publish something that uses or refers to this work, please cite the
following paper:

@inproceedings{Hardmeier:2012a,
	Author = {Hardmeier, Christian and Nivre, Joakim and Tiedemann, J\"{o}rg},
	Booktitle = {Proceedings of the 2012 Joint Conference on Empirical
		Methods in Natural Language Processing and Computational Natural Language
		Learning},
	Month = {July},
	Pages = {1179--1190},
	Publisher = {Association for Computational Linguistics},
	Title = {Document-Wide Decoding for Phrase-Based Statistical Machine Translation},
	Address = {Jeju Island, Korea},
	Year = {2012}}

Requests and comments about this software can be addressed to
	docent@stp.lingfil.uu.se


DOCENT LICENSE
==============
All code included in docent, except for the contents of the 'external'
directory, is copyright 2012 by Christian Hardmeier. All rights reserved.

Docent is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

Docent is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with Docent. If not, see <http://www.gnu.org/licenses/>.


SNOWBALL STEMMER LICENSE
========================
Docent contains code from the Snowball stemmer library downloaded from
http://snowball.tartarus.org/ (in external/libstemmer_c). This is what
the Snowball stemmer website says about its license (as of 4 Sep 2012):

"All the software given out on this Snowball site is covered by the BSD License
(see http://www.opensource.org/licenses/bsd-license.html ), with Copyright (c)
2001, Dr Martin Porter, and (for the Java developments) Copyright (c) 2002,
Richard Boulton.

Essentially, all this means is that you can do what you like with the code,
except claim another Copyright for it, or claim that it is issued under a
different license. The software is also issued without warranties, which means
that if anyone suffers through its use, they cannot come back and sue you. You
also have to alert anyone to whom you give the Snowball software to the fact
that it is covered by the BSD license.

We have not bothered to insert the licensing arrangement into the text of the
Snowball software."


BUILD INSTRUCTIONS
==================
See file doc/Build.txt


USAGE
=====
See also file doc/Usage.txt

1. File input formats

Docent accepts the NIST-XML and the MMAX formats for document input. Output is
always produced in the NIST-XML format. MMAX input requires you to provide both
a NIST-XML file and an MMAX directory, since the NIST-XML input file is used as
a template for the output.
NIST-XML output without MMAX can be used for models that process unannotated
plain text input. MMAX input can be used to provide additional annotations, e.g.
if the input has been annotated for coreference with a tool like BART
(http://www.bart-coref.org/).

2. Decoder configuration

The decoder uses an XML configuration file format. There are two example
configuration files in tests/config.
The <random> tag specifies the initialisation of the random number generation.
When it is empty (<random/>), a random seed value is generated and logged at the
beginning of the decoder run. In order to rerun a specific sequence of
operations (for debugging), a seed value can be provided as shown in one of the
example configuration files.

The only phrase table format currently supported is the binary phrase table
format of moses (generated with processPhraseTable). Note that Docent, unlike
moses, doesn't have a parameter to enforce a limit on the number of translations
for a given phrase that are loaded from the phrase table. We recommend that you
filter your phrase tables with the filter-pt tool from the Moses distribution
and a setting like "-n 30" before using them with Docent. Otherwise you may
experience very poor performance.

Language models should be in KenLM's probing hash format (other formats
supported by KenLM can potentially be used as well).

There is some more documentation about the configuration file format on Docent's
Wiki page: https://github.com/chardmeier/docent/wiki/Docent-Configuration

3. Invoking the decoder

There are three main docent binaries:

docent		for decoding a corpus on a single machine without producing
		intermediate output

lcurve-docent	for producing learning curves

mpi-docent	for decoding a corpus on an MPI cluster

I recommend that you use lcurve-docent for experimentation.

Usage: lcurve-docent {-n input.xml|-m input.mmaxdir input.xml} config.xml outputstem

Use the -n or -m switch to provide NIST-XML input only or NIST-XML input
combined with MMAX annotations, respectively.
The file config.xml contains the decoder configuration.

The decoder produces output files for the initial search state and intermediate
states whenever the step number is equal to a power of two, starting at 256.
Output files are named
outstem.000000000.xml
outstem.000000256.xml
etc.

4. Extending the decoder

To implement new feature functions, start with one of the existing.
SentenceParityModel.cpp contains a very simple proof-of-concept feature function that
promotes length parity per document (i.e. all sentences in a document should
consistently have either odd or even length).
If you're planning to implement something more complex, NgramModel.cpp or
SemanticSpaceLanguageModel.cpp may be good places to start.

Good luck!
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