OLAC - Open Language Archives Community

Link: http://www.language-archives.org/

Intro: OLAC, the Open Language Archives Community, is an international partnership of institutions and individuals who are creating a worldwide virtual library of language resources by: (i) developing consensus on best current practice for the digital archiving of language resources, and (ii) developing a network of interoperating repositories and services for housing and accessing such resources.

Visualgdb

Link: http://visualgdb.com/tutorials/linux/import/

Intro: This tool will help how to import a Linux project from a Linux machine to Visual Studio to build and debug it remotely.

This tool is probably comfortable for a Windows-based and Visual Studio fan who wants to compile a project remotely on Linux.

In terms of point of view of a Linux coder, it's not a good way. You may learn how to use gdb with CLI programming instead.

IR book by Bruce Croft

Title: Search Engines Information Retrieval in Practice by Prof. Bruce Croft
Link: http://ciir.cs.umass.edu/irbook/

Intro: This book provides an overview of the important issues in information retrieval, and how those issues affect the design and implementation of search engines. Not every topic is covered at the same level of detail. The focus is on some of the most important alternatives to implementing search engine components and the information retrieval models underlying them. The target audience for the book is advanced undergraduates in computer science, although it is also a useful introduction for graduate students. (from the link)

 

NAACL 2015 papers

Link: http://naacl.org/naacl-hlt-2015/papers.html

Here is my subjective list of remarkable papers relating to MT research:

 

*** Neural Machine Translation

 

Paul Baltescu and Phil Blunsom. "Pragmatic Neural Language Modelling in Machine Translation"

 

Adrià de Gispert, Gonzalo Iglesias, Bill Byrne. "Fast and Accurate Preordering for SMT using Neural Networks"

 

*** Continuous Models for Statistical Machine Translation

 

Frédéric Blain, Fethi Bougares, Amir Hazem, Loïc Barrault, Holger Schwenk. "Continuous Adaptation to User Feedback for Statistical Machine Translation"

 

Kai Zhao, Hany Hassan, Michael Auli. "Learning Translation Models from Monolingual Continuous Representations"

 

*** Multi-language Translation 

 

Raj Dabre, Fabien Cromieres, Sadao Kurohashi, Pushpak Bhattacharyya. "Leveraging Small Multilingual Corpora for SMT Using Many Pivot Languages"

 

*** Video to Text Translation 

 

Subhashini Venugopalan, Huijuan Xu, Jeff Donahue, Marcus Rohrbach, Raymond Mooney, Kate Saenko. "Translating Videos to Natural Language Using Deep Recurrent Neural Networks" 

 

*** Others

 

Jonathan H. Clark, Chris Dyer, Alon Lavie. "Locally Non-Linear Learning for Statistical Machine Translation via Discretization and Structured Regularization"

 

Graham Neubig, Philip Arthur, Kevin Duh. "Multi-Target Machine Translation with Multi-Synchronous Context-free Grammars"

 

Aurelien Waite and Bill Byrne. "The Geometry of Statistical Machine Translation"

Other papers are also worth reading:

*** News Processing

Areej Alhothali and Jesse Hoey. "Good News or Bad News: Using Affect Control Theory to Analyze Readers' Reaction Towards News Articles"

 

 

Python wrapper for online translators

If you want to use Google Translate and Microsoft Bing Translate for free, you may consider the following Python-based wrappers:

+ Code: Google Translate; Bing Translate

+ Samples:

# Google Translate
import googletrans
gs = googletrans.Googletrans()

languages = gs.get_languages()
print(languages['en'])

print(gs.translate('hello', 'de'))
print(gs.translate('hello', 'zh'))
print(gs.translate('hello', 'vi'))

print(gs.detect('some English words'))

 

#Bing Translate

from mstranslator import Translator

translator =
Translator('cdvhoang', 'HlUUMftdkETWa8E9/jzD4l1CzC8sOhRSJxH+kk0MDBg=')

print(translator.translate('hello', lang_from='en', lang_to='vi')) 

 *** Please note that I don't encourage to use the wrapper for Google Translate because you should respect and pay for using its service (simply it's now commercialized ^_^).

 

ACL 2015 papers

Link: http://acl2015.org/accepted_papers.html

Here is my subjective list of remarkable papers relating to MT research:

*** Conventional Statistical Machine Translation

 A CONTEXT-AWARE TOPIC MODEL FOR STATISTICAL MACHINE TRANSLATION
Jinsong Su, Deyi Xiong, Yang Liu, Xianpei Han, Hongyu Lin and Junfeng Yao

NON-LINEAR LEARNING FOR STATISTICAL MACHINE TRANSLATION
Shujian Huang, Huadong Chen, Xinyu Dai and Jiajun Chen

MULTI-TASK LEARNING FOR MULTIPLE LANGUAGE TRANSLATION
Daxiang Dong, Hua Wu, Wei He, Dianhai Yu and Haifeng Wang

WHAT’S IN A DOMAIN? ANALYZING GENRE AND TOPIC DIFFERENCES IN STATISTICAL MACHINE TRANSLATION
Marlies van der Wees, Arianna Bisazza, Wouter Weerkamp and Christof Monz

*** Neural Machine Translation

ADDRESSING THE RARE WORD PROBLEM IN NEURAL MACHINE TRANSLATION
Thang Luong, Ilya Sutskever, Quoc Le, Oriol Vinyals and Wojciech Zaremba

ENCODING SOURCE LANGUAGE WITH CONVOLUTIONAL NEURAL NETWORK FOR MACHINE TRANSLATION
Fandong Meng, Zhengdong Lu, Mingxuan Wang, Hang Li, Wenbin Jiang and Qun Liu

IMPROVED NEURAL NETWORK FEATURES, ARCHITECTURE AND LEARNING FOR STATISTICAL MACHINE TRANSLATION
Hendra Setiawan, Zhongqiang Huang, Jacob Devlin, Thomas Lamar and Rabih Zbib

NON-PROJECTIVE DEPENDENCY-BASED PRE-REORDERING WITH RECURRENT NEURAL NETWORK FOR MACHINE TRANSLATION
Antonio Valerio Miceli Barone

ON USING VERY LARGE TARGET VOCABULARY FOR NEURAL MACHINE TRANSLATION
Sebastien Jean, Kyunghyun Cho, Roland Memisevic and Yoshua Bengio

CONTEXT-DEPENDENT TRANSLATION SELECTION USING CONVOLUTIONAL NEURAL NETWORK
Baotian Hu, Zhaopeng Tu, Zhengdong Lu and Hang Li

*** Machine Translation Evaluation and Quality Estimation

ONLINE MULTITASK LEARNING FOR MACHINE TRANSLATION QUALITY ESTIMATION
José G. C. de Souza, Matteo Negri, Marco Turchi and Elisa Ricci

PAIRWISE NEURAL MACHINE TRANSLATION EVALUATION
Francisco Guzmán, Shafiq Joty, Lluís Màrquez and Preslav Nakov

EVALUATING MACHINE TRANSLATION SYSTEMS WITH SECOND LANGUAGE PROFICIENCY TESTS
Takuya Matsuzaki, Akira Fujita, Naoya Todo and Noriko H. Arai

Some notes:

*** According to my observation, there are some research trends depending on data characteristics:
- very big data
- heterogeneous data
- multi-lingual data

*** And of course, deep learning research is still very hot.

Torch vs. Theano vs. Caffe

Link: http://fastml.com/torch-vs-theano/

Here is my summary:

- Torch and Theano are better to be used for research purpose on deep learning (DL) whereas Caffe is more scaled for DL application development.

- Torch and Theano are competitive in terms of speech and performance via different benchmarks. Hence, choosing one of them depends the ease of use from users.

(to be updated)