computers. It was in the early 1990s that machine translation began to make the transition away from large mainframe computers toward personal computers and workstations. Two companies that led the PC market for a time were Globalink and MicroTac, following which a merger of the two companies (in December 1994) was found to be in the corporate interest of both. Intergraph and Systran also began to offer PC versions around this time. Sites also became available on the internet, such as AltaVista's Babel Fish (using Systran technology) and Google Language Tools (also initially using Systran technology exclusively). 2000s The field of machine translation has seen major changes in the 2000s. A large amount of research was done into statistical machine translation and example-based machine translation. In the area of speech translation, research was focused on moving from domain-limited systems to domain-unlimited translation systems. In different research projects in Europe (like TC-STAR) and in the United States (STR-DUST and DARPA Global autonomous language exploitation program), solutions for automatically translating Parliamentary speeches and broadcast news was developed. In these scenarios the domain of the content was no longer limited to any special area, but rather the speeches to be translated cover a variety of topics. The French-German project Quaero investigated the possibility of making use of machine translations for a multi-lingual internet. The project sought to translate not only webpages, but also videos and audio files on the internet. 2010s The past decade witnessed neural machine translation (NMT) methods replace statistical machine translation. The term neural machine translation was coined by Bahdanau et al and Sutskever et al who also published the first research regarding this topic in 2014. Neural networks only needed a fraction of the memory needed by statistical models and whole sentences could be modeled in an integrated manner. The first large scale NMT was launched by Baidu in 2015, followed by Google Neural Machine Translation (GNMT) in 2016. This was followed by other translation services like DeepL Translator and the adoption of NMT technology in older translation services like Microsoft translator. Neural networks use a single end to end neural network architecture known as sequence to sequence (seq2seq) which uses two recurrent neural networks (RNN). An encoder RNN and a decoder RNN. Encoder RNN uses encoding vectors on the source sentence and the decoder RNN generates the target sentence based on the previous encoding vector. Further advancements in