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.

Wikipedia and LM

Link: http://trulymadlywordly.blogspot.sg/2011/03/creating-text-corpus-from-wikipedia.html
Intro: How to leverage Wikipedia repository to create huge LM data.

Available Data from the CommonCrawl

Link: http://statmt.org/ngrams/
Intro: Multi-language data used for training large-scale LMs crawled from CommonCrawl.

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 ...)

YALIGN

Link: http://yalign.machinalis.com/

Intro: Statistical Machine Translation relies on parallel corpora for training translation models. However these corpora are limited and take time to create. Yalign is designed to automate this process by finding sentences that are close translation matches from comparable corpora. This opens up avenues for harvesting parallel corpora from sources like translated documents and the web.

Multeval

Link: https://github.com/jhclark/multeval

Intro: MultEval takes machine translation hypotheses from several runs of an optimizer and provides 3 popular metric scores, as well as, standard deviations (via bootstrap resampling) and p-values (via approximate randomization). This allows researchers to mitigate some of the risk of using unstable optimizers such as MERT, MIRA, and MCMC. It is intended to help in evaluating the impact of in-house experimental variations on translation quality; it is currently not setup to do bake-off style comparisons (bake-offs can't require multiple optimizer runs nor a standard tokenization).

Related: http://www.ark.cs.cmu.edu/MT/ (Code for Statistical Significance Testing for MT Evaluation Metrics)

Web translation service

Link: http://itranslate4.eu/en/chat

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

ACCURAT Toolkit

Link: http://www.accurat-project.eu/

Intro: The ACCURAT project (http://www.accurat-project.eu/) is pleased to announce the release of ACCURAT Toolkit - a collection of tools for comparable corpora collection and multi-level alignment and information extraction from comparable corpora. By using the ACCURAT Toolkit, users may obtain:

- Comparable corpora from the Web (current news corpora, filtered Wikipedia corpora, and narrow domain focussed corpora);

- Comparable document alignments;

- Semi-parallel sentence/phrase mapping from comparable corpora (for SMT training purposes or other tasks);

- Translated terminology extracted and mapped from bilingual comparable corpora;

- Translated named entities extracted and mapped from bilingual comparable corpora.

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.

PET - Post-Editing Translation Tool

Link: http://pers-www.wlv.ac.uk/~in1676/pet/
Intro: PET is a stand-alone, open-source (under LGPL) tool written in Java that should help you post-edit and assess machine or human translations while gathering detailed statistics about post-editing time amongst other effort indicators.

WIT3 - Web Inventory of Transcribed and Translated Talks

Link: https://wit3.fbk.eu/

Intro: WIT3 - acronym for Web Inventory of Transcribed and Translated Talks - is a ready-to-use version for research purposes of the multilingual transcriptions of TED talks. 

Since 2007, the TED Conference has been posting on its website all video recordings of its talks, English subtitles and their translations in more than 80 languages. In order to make this collection of talks more effectively usable by the research community, the original textual contents are redistributed here, together with MT benchmarks and processing tools.

Parallel Text Mining for SMT

Problem: given a relatively large collection of parallel texts and a state-of-the-art SMT system, how to incrementally & automatically mine the parallel texts available on the Web. The newly added texts should ensure to improve the current SMT system.

Papers related:

1) Large Scale Parallel Document Mining for Machine Translation. COLING 2010. Link.
2) TBA

EVBCorpus - English-Vietnamese Bilingual Corpus

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

Tbot - Translation Buddy for Windows Live Messenger

http://www.microsofttranslator.com/user/bot/

Tbot is an automated buddy that provides translations for Windows Live Messenger. It was first launched in 2008 as a prototype and has since become immensely popular. You can have one-on-one conversations with Tbot or invite friends who speak different languages with Tbot translating for you.

Statistical Significance Testing

http://www.ark.cs.cmu.edu/MT/

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