TechGist05

This is a guest post from Luigi Muzii which is basically made up primarily of the speaker notes of his presentation at the ELIA Together 2018 conference. I believe that Luigi has wise words and represent best practices thinking and thus offer it on this blog forum. While some of his advice might seem obvious to some, I am always struck by often these very commonsensical recommendations are either overlooked or simply ignored willfully. As generic MT improves, I think it becomes more and more important for enterprises to consider bringing rampant, uncontrolled MT use by employees and outsourced translators under control.  As always the emphasis of bold text in the post is my doing. ==== The presentation was designed to provide some practical advice about tackling the challenges that freelancers, project managers, and translation buyers face when approaching MT, implementing MT or running MT and post-editing projects. Sharing efforts to get the most from MT+PE from Luigi Muzii ...

This is a guest post by Valeria Cannavina, Project Coordinator at Donnelley Language Solutions, adopting the Common Sense Advisory’s Localization Maturity Model (LMM) which is itself an adaptation of the software industry’s Capability Maturity Model (CMM). The resulting Machine Translation Maturity Model (MTMM) is a way of assessing the users’ understanding of the technology, and whether they are using it in an efficient and effective manner, properly linking it to other organizational processes. Valeria provides a framework for businesses to “identify where they are and what they can do to either significantly or modestly improve their existing production model to maximize the value that MT can provide to their organizations.”  This is, however, a perspective that is quite localization-centric, and process alignment for a global Enterprise MT service that might be used by thousands of users, across an enterprise to translate hundreds of millions of words could be quite different. ...

The Emerging MT-Driven Translation Market Opportunity As we roll on into the New Year, it is clear that machine translation is now pervasive, universally available, and easily accessible to millions across the globe on all kinds of digital devices. It is often even accessible when we are not connected to the web. This widespread access to, and use of MT is true across the globe and recent advances in translation quality from Neural MT have raised the profile of MT all over again. And while Google is a large provider of generic MT, it is not dominant across the globe, and we see that regional MT portals dominate local markets, e.g. Baidu in China, Naver in Korea, and Yandex in Russia. By my very conservative estimates, the sheer volume of translation done is astonishing, and I think the global daily use of MT is easily in excess of 500 billion words a day. This amounts to approximately 185 trillion words a year, and this is if MT use does not continue to grow even further as new popula...

Here are some interesting results from guest writer Antonio Toral, who provided us a good broad look at how NMT was doing relative to PBMT last year. His latest research investigates the potential for NMT in assisting with the translation of Literary Texts. While NMT is still a long way from human quality, it is interesting to note that NMT very consistently beats SMT even at the BLEU score level. At th eresearch level this is a big deal. Given that BLEU scores tend to favor SMT systems naturally, this is especially promising, and the results are probably quite strikingly better when compared by human reviewers. I have also included another short post Antonio did on the detailed human review of NMT vs SMT output to show those who still doubt that NMT is the most likely way forward for any MT project today. ---------------------- Neural networks have revolutionised the field of Machine Translation (MT). Translation quality has improved drastically over that of the previous dominant ap...

This is a guest post by Laura Casanellas @ LauraCasanellas  describing her journey with language technology. She raises some good questions for all of us to ponder over the coming year.   Neural MT is all the rage now and it now appears in almost every translation industry discussion we see today. Sometimes depicted as a terrible job-killing force and sometimes as a savior, though I would bet that it is neither. Hopefully, the hype subsides and we start focusing on solving issues that enable high-value deployments. I have been interviewed by a few people about NMT technology in the last month, so expect to see even more on NMT, and we continue to see that GAFA and the Chinese/Korean giants (Baidu, Alibaba, Naver) also introduce NMT offerings.  Open source toolkits for NMT proliferate, training data is easier to acquire, and hardware options for neural net and deep learning experimentation continue to expand.  It is very likely that we will see even more generic NM...

This is the final post for the 2017 year, a guest post by Jean Senellart who has been a serious MT practitioner for around 40 years, with deep expertise in all the technology paradigms that have been used to do machine translation. SYSTRAN has recently been running tests building MT systems with different datasets and parameters to evaluate how data and parameter variation affect MT output quality. As Jean said: " We are continuously feeding data to a collection of models with different parameters – and at each iteration, we change the parameters. We have systems that are being evaluated in this setup for about 2 months and we see that they continue to learn." This is more of a vision statement about the future evolution of this (MT) technology, where they continue to learn and improve, rather than a direct reporting of experimental results, and I think is a fitting way to end the year in this blog. It is very clear to most of us that deep learning based approaches are the wa...