Iconic Translation Machines (Iconic), a leading Machine Translation (MT) software and solutions provider, along with the ADAPT Centre, recently announced their participation in the 20th Annual Conference of the European Association for Machine Translation (EAMT) taking place in Prague, 29-31 May. The prestigious event will be held at the Faculty of Mathematics and Physics at Charles University in Prague. The conference showcases the latest in academic MT research and development, and features presentations examining the best practices from the global localisation industry.
Iconic and the ADAPT Centre will present the findings of a joint research project carried out on Neural Machine Translation (NMT), including a comparative evaluation of NMT engines with Iconic’s existing custom production MT engines. Human evaluation was also incorporated into the study to compare and contrast the benefits and disadvantages of the various technologies.
The findings of this research strengthen the belief that more research is required to further address the practical limitations of NMT. Post-doctoral Researcher at ADAPT Dr Shelia Castilho points out: “The findings show that, while NMT is really promising particularly in terms of fluency, there is still a lot of research and evaluation required for certain languages and domains before it can outright replace the current state-of-the-art.”
The research findings suggest that there is cause for great optimism given results published to date for NMT, but that there are still many cases where existing Statistical Machine Translation (SMT) and Hybrid approaches achieve better quality for certain language and content types, based on human evaluations.
Some of the key features of this technology include, the capability to greatly improve the fluency of MT output, particularly for complex language pairs. NMT output contains fewer word order errors and fewer inflectional morphology errors in all target languages, leading to greater fluency. The engines within NMT produce peculiar errors, such as omitting parts of sentences, which are difficult to predict and resolve. In addition to these features, NMT has demonstrated the capability to handle the complexities of languages that are difficult for MT such as Korean and Japanese. But, there is still not as much flexibility for customisation and importantly, directly addressing errors in the output.
The ADAPT led, Iconic NMT engines were implemented using an ensemble of attention-based models trained on different combinations of in-domain and general data. The results have further supported Iconic’s recent development of incorporating NMT into its proprietary technology only in cases where it can provide significant benefit for commercial applications in the short-term. Iconic combines the benefits of NMT with the suite of its own existing technology components and expertise in order to overcome any NMT shortcomings.
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