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.
