This proposal is an extension to the project “Generation in the Context of Machine Translation”, which is one of the projects done in the Johns Hopkins University Center for Speech and Language Processing 2002 summer workshop (WS02GMT). We first address some issues observed in WS02GMT which possibly leads to detrimental effects in machine translation performance. And hence we propose a new appr...

We investigate the potential of attention-based neural machine translation in simultaneous translation. We introduce a novel decoding algorithm, called simultaneous greedy decoding, that allows an existing neural machine translation model to begin translating before a full source sentence is received. This approach is unique from previous works on simultaneous translation in that segmentation a...

The MateCat tool is currently under development and tested in real industrial workflows as well as by many MT researchers around the world. At MT Summit 2013, besides this project overview presentation, both the tool and recent advances by the project will be presented through one tutorial, a stand at the conference exhibition, three technical presentations at the main conference, and one prese...

When building machine translation systems, one often needs to make the best out of heterogeneous sets parallel data in training, and robustly handle inputs from unexpected domains testing. This multi-domain scenario has attracted a lot recent work that fall under general umbrella transfer learning. In this study, we revisit translation, with aim formulate motivations for developing such systems...

Rule-based machine translation analyzes source-language sentences using large-scale linguistic knowledge that is given by the developer beforehand. However, it is difficult to give complete linguistic knowledge to the system ex ante because natural language has various linguistic phenomena. Therefore, we worked to develop learning-based machine translation. In learning-based machine translation...

Neural machine translation has become a major alternative to widely used phrase-based statistical machine translation. We notice however that much of research on neural machine translation has focused on European languages despite its language agnostic nature. In this paper, we apply neural machine translation to the task of Arabic translation (Ar↔En) and compare it against a standard phrase-ba...

With the development of the Internet environments, more and more language services become accessible for common people. However, the gap between human translators and machine translators remains huge especially for the domain of localization processes that requires high translation quality. Although efforts of combining human and machine translators for supporting multilingual communication hav...