Puck - GPU-based natural language parser

Link: https://github.com/dlwh/puck

Intro: Puck is a high-speed, high-accuracy parser for natural languages. It's (currently) designed for use with grammars trained with the Berkeley Parser and on NVIDIA cards. On recent-ish NVIDIA cards (e.g. a GTX 680), around 400 sentences a second with a full Berkeley grammar for length <= 40 sentences.

Puck is only useful if you plan on parsing a lot of sentences. On the order of a few thousand. Also, it's designed for throughput, not latency.

TCLAP - Templatized C++ Command Line Parser Library

Link: http://tclap.sourceforge.net/

Intro: TCLAP is a small, flexible library that provides a simple interface for defining and accessing command line arguments. It was intially inspired by the user friendly CLAP libary. The difference is that this library is templatized, so the argument class is type independent. Type independence avoids identical-except-for-type objects, such as IntArg, FloatArg, and StringArg. While the library is not strictly compliant with the GNU or POSIX standards, it is close.

OpenMPI

Link: http://www.open-mpi.org/

Intro: The Open MPI Project is an open source Message Passing Interface implementation that is developed and maintained by a consortium of academic, research, and industry partners. Open MPI is therefore able to combine the expertise, technologies, and resources from all across the High Performance Computing community in order to build the best MPI library available. Open MPI offers advantages for system and software vendors, application developers and computer science researchers.

Machine Learning podcast

Link: http://www.thetalkingmachines.com/

Intro: Talking Machines is your window into the world of machine learning. 

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.