TinyTM

Link: http://tinytm.sourceforge.net/en/index.html
Intro: TinyTM - Open-Source Translation Memory

NiuTrans: A Statistical Machine Translation System

Link: http://www.nlplab.com/NiuPlan/NiuTrans.html#introduction

Intro: NiuTrans is an open-source statistical machine translation system developed by the Natural Language Processing Group at Northeastern University, China. The NiuTrans system is fully developed in C++ language. So it runs fast and uses less memory. Currently it has already supported phrase-based, hierarchical phrase-based and syntax-based (string-to-tree, tree-to-string and tree-to-tree) models for research-oriented studies.

Paper (ACL 2012 Demo): http://www.nlplab.com/NiuPlan/paper/ACL2012-NiuTrans.pdf

Docent - Document-level SMT

Link: https://github.com/chardmeier/docent/wiki

Intro: 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).
Paper: https://aclweb.org/anthology-new/D/D12/D12-1108.pdf

Champollion Tool Kit - Text Sentence Aligner

Link: http://champollion.sourceforge.net/

Intro: Built around LDC's champollion sentence aligner kernel, Champollion Tool Kit (CTK) aims to providing ready-to-use parallel text sentence alignment tools for as many language pairs as possible.

Champollion depends heavily on lexical information, but uses sentence length information as well. A translation lexicon is required. Past experiments indicate that champollion's performance improves as the translation lexicon become larger.

Subtitle Translation

Subtitle corpus: http://opus.lingfil.uu.se/ (more)

Tool: http://www.jaist.ac.jp/~s1010205/translate_subs_doc/

*** Google Translation API is no longer freely available. Can we use state-of-the-art SMT techniques to build a subtitle SMT system by ourself? What are challenges???

EVBCorpus - English-Vietnamese Bilingual Corpus

http://code.google.com/p/evbcorpus/

SMT without Parallel Corpora

Toward Statistical Machine Translation without Parallel Corpora (link). EMNLP 2011.

Bilingual Sentence Aligner

Tool by Microsoft

TER-Plus (TERp)

TERp: http://www.umiacs.umd.edu/~snover/terp/

Intro
TERp is an automatic evaluation metric for Machine Translation, which takes as input a set of reference translations, and a set of machine translation output for that same data. It aligns the MT output to the reference translations, and measures the number of 'edits' needed to transform the MT output into the reference translation. TERp is an extension of TER (Translation Edit Rate) that utilizes phrasal substitutions (using automatically generated paraphrases), stemming, synonyms, relaxed shifting constraints and other improvements.

Open Source Machine Translation System Combination

MANY: http://www-lium.univ-lemans.fr/~barrault/MANY/

Intro

MANY is an MT system combination software which architecture is described is the following picture :

The combination can be decomposed into three steps

• 1-best hypotheses from all M systems are aligned in order to build M confusion networks (one for each system considered as backbone).
• All CNs are connected into a single lattice. The first nodes of each CN are connected to a unique first node with probabilities equal to the priors probabilities assigned to the corresponding backbone. The final nodes are connected to a single final node with arc probability of one.
• A token pass decoder is used along with a language model to decode the resulting lattice and the best hypothesis is generated.
  

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Cheers,
Vu

System Combination for Machine Translation

This post is to collect papers regarding to system combination problem for Machine Translation systems. (collect everything first, filter later then)

1) Felipe Sánchez-Martínez. Choosing the best machine translation system to translate a sentence by using only source-language information. In Proceedings of the 15th Annual Conference of the European Associtation for Machine Translation, p. 97-104, May 30-31, 2011, Leuven, Belgium.

2) Víctor M. Sánchez-Cartagena, Felipe Sánchez-Martínez, Juan Antonio Pérez-Ortiz. Enriching a statistical machine translation system trained on small parallel corpora with rule-based bilingual phrases. In Proceedings of the International Conference on Recent Advances in Natural Language Processing, RANLP 2011, p ?-?, September 12-14, 2011, Hissar, Bulgaria (forthcoming)


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Cheers,
Vu

Open Toolkit for Automatic MT (Meta-) Evaluation

Asiya: http://www.lsi.upc.edu/~nlp/Asiya/

Asiya has been designed to assist both system and metric developers by offering a rich repository of metrics and meta-metrics. Asiya has been developed at TALP Research Center NLP group , in Universitat Politècnica de Catalunya, as an evolution, extension, refactoring, and finally a replacement for its predecessor, IQMT.

Hybrid Example-based and Statistical MT System

Cunei: http://www.cunei.org/about/

Cunei is a hybrid platform for machine translation that draws upon the depth of research in Example-Based MT (EBMT) and Statistical MT (SMT). In particular, Cunei uses a data-driven approach that extends upon the basic thesis of EBMT--that some examples in the training data are of higher quality or are more relevant than others. Yet, it does so in a statistical manner, embracing much of the modeling pioneered by SMT, allowing for efficient optimization. Instead of using a static model for each phrase-pair, at run-time Cunei models each example of a phrase-pair in the corpus with respect to the input and combines them into dynamic collections of examples. Ultimately, this approach provides a more consistent model and a more flexible framework for integration of novel run-time features.

Document Translation Retrieval

Sánchez-Martínez, F., R.C. Carrasco. Document translation retrieval based on statistical machine translation techniques. In Applied Artificial Intelligence 25(5):329-340

http://code.google.com/p/doctrans/downloads/list

Hierarchical Statistical Machine Translation Toolkits

1) Joshua

2) Jane

3) Moses (also include Hierarchical SMT part)

(to be updated)

Error Analysis for Machine Translation Output

This post is to collect some papers in the literature referring to error analysis of (Statistical) Machine Translation Output. I aim to apply for English-Vietnamese translation outputs.

1) BLAST: http://www.ida.liu.se/~sarst/blast/

(Demo paper at ACL'2011: http://www.aclweb.org/anthology/P/P11/P11-4010.pdf)

2)

Maja Popovic et al. Towards Automatic Error Analysis of Machine Translation Output. (Computational Linguistics 2011)

3)

Mireia F. et al. Overcoming statistical machine translation limitations: error analysis and proposed solutions for the Catalan–Spanish language pair. LREC 2011.

4)

S. Condon. Machine Translation Errors: English and Iraqi Arabic. TALIP 2011.

5)

Maja Popović, Adrià de Gispert, Deepa Gupta, Patrik Lambert, Hermann Ney, José B. Mariño and Rafael Banchs. Morpho-syntactic Information for Automatic Error Analysis of Statistical Machine Translation Output. HLT/NAACL Workshop on Statistical Machine Translation, pages 1-6, New York, NY, June 2006.

Maja Popović and Hermann Ney. Error Analysis of Verb Inflections in Spanish Translation Output. TC-Star Workshop on Speech-to-Speech Translation, pages 99-103, Barcelona, Spain, June 2006.

David Vilar et al. Error Analysis of Statistical Machine Translation Output. LREC 2006.

Moses on Windows

http://www.statmt.org/moses/?n=Moses.FAQ#ntoc9

http://ssli.ee.washington.edu/people/amittai/Moses-on-Win7.pdf

OPUS - The Open Parallel Corpus

http://opus.lingfil.uu.se/

Intro

OPUS is a growing collection of translated texts from the web. In the OPUS project we try to convert and align free online data, to add linguistic annotation, and to provide the community with a publicly available parallel corpus. OPUS is based on open source products and the corpus is also delivered as an open content package. We used several tools to compile the current collection. All pre-processing is done automatically. No manual corrections have been carried out.

It's amazing. It's free!

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Cheers,
Vu

A unified framework for phrase-based, hierarchical, syntax-based SMT

http://homepages.inf.ed.ac.uk/alopez/papers/iwslt09-hoang+etal.pdf

cdec decoder

http://cdec-decoder.org/index.php?title=Main_Page