Word Aligners for Machine Translation

Here is a not-complete list of word aligners used for Machine Translation:

1) Unsupervised Aligners
- GIZA++
- fast_align (with cdec)
- pialign
- BerkeleyAligner

2) Supervised Aligners
- BerkeleyAligner
- NILE

(to be updated ...)

Multi-Task Learning toolkit

1) MALSAR
Link: http://www.public.asu.edu/~jye02/Software/MALSAR/

Intro: the MALSAR (Multi-tAsk Learning via StructurAl Regularization) package includes the following multi-task learning algorithms:

• Mean-Regularized Multi-Task Learning
• Multi-Task Learning with Joint Feature Selection
• Robust Multi-Task Feature Learning
• Trace-Norm Regularized Multi-Task Learning
• Alternating Structural Optimization
• Incoherent Low-Rank and Sparse Learning
• Robust Low-Rank Multi-Task Learning
• Clustered Multi-Task Learning
• Multi-Task Learning with Graph Structures
• Disease Progression Models
• Incomplete Multi-Source Fusion (iMSF)
• Multi-Stage Multi-Source Fusion
• Multi-Task Clustering

2)
Link: http://klcl.pku.edu.cn/member/sunxu/software/MultiTask.zip
Intro: This is a general purpose software for online multi-task learning. The online multi-task learning is mainly based on Conditional Random Fields (CRF) model and Stochastic Gradient Descent (SGD) training.

I am going to deepen this technique for machine translation and domain adaptation.

MTTK - Machine Translation Toolkit

Link: http://mi.eng.cam.ac.uk/~wjb31/distrib/mttkv1/

Intro: MTTK is a collection of software tools for the alignment of parallel text for use in Statistical Machine Translation. With MTTK you can ...

• Align document translation pairs at the sentence or sub-sentence level, sometimes known as chunking. This is a useful pre-processing step to prepare collections of translations for use in estimating the parameters of complex alignment models. Sub-sentence alignment in particular makes it possible to segment long sentences into shorter aligned segments that otherwise would have to be discarded.
• Train statistical models for parallel text alignment.  The following models are supported : 
• IBM Model-1 and Model-2
• Word-to-Word HMMs  
• Word-to-Phrase HMMs ,  with bigram translation probabilities 
• Parallelize your model training procedures. If you have multiple CPUs available,  you can partition your translation training texts into subsets,  thus speeding up iterative parameter re-estimation procedures and reducing the amount of memory needed in training. This is done under exact EM-based parameter estimation procedures.
• Generate word-to-word and word-to-phrase alignments of parallel text. MTTK can generate Viterbi alignments of parallel text (both training text and other texts) under the supported alignment models.
• Extract word-to-word translation tables from aligned bitext and from the estimated models.
• Extract phrase-to-phrase translation tables (phrase-pair inventories) from aligned parallel text.
• Use the HMM alignment models to induce phrase translations under its statistical models.   Phrase-pair induction can generate richer inventories of phrase translations than can be extracted from Viterbi alignments.
• Edit the C++ source code to implement your own estimation and alignment procedures.