Machine Translation Master Course Newsletter – Issue 3

Machine Translation Master Course Newsletter – Issue 3

Machine Translation Master Class Newsletter – Issue 3

 

As the Instructor for the Machine Translation Master Class from The Localization  Institute, I am very happy to continue to share with you my ideas about the relationship  between humans and machines. In this issue, we will focus on the role of humans in an MT deployment process, in which there are three key players, namely, project managers, linguists, and MT suppliers. 

Project managers: how can you leverage machine power effectively?  

As we grow our company, we are also growing our linguistic assets such as terminology and translation memory. When our language data have increased greatly in quantity and quality, it is very natural that we are thinking of more ways to leverage our resources, and of course, one of the best ways is to apply them to a machine translation system. As a result, we are bringing a new player into the field, who is so different from us.  

As the manager of the whole team, it makes much more sense that you have a better  knowledge of this new player and also help other team members understand how to  work with it in the MT deployment process. This is not easy, as machines break the existing balance of your workflow, including how and what you need to communicate  with your team members, your colleagues in other departments, your clients, your LSPs, and last but not least, your MT suppliers. It is indeed challenging but interesting to manage this dynamic situation. To this end, possessing the most relevant knowledge of machine translation and data management is a must. It helps you make plans to meet the needs and expectations from both humans and machines.  

Linguists: a “translation system” to be compared with MT systems?  

As a translator, at some point, if you happen to find yourself being evaluated with some MT systems, don’t panic or feel bitter. Behind machines, there is such a “collective wisdom” obtained from millions or billions of language use cases by people. The amount of data an MT engine can process within a short period of time might be greater than what a human translator can process in his or her whole life. It is not surprising if you win or lose this game. This cannot change the fact that each individual is a unique existence of this world.  

In a more practical sense, it might be a good idea that we start to think about how to  make use of what you are good at and which direction we shall move in. This situation exists not only in the language industry, but for each and every profession. I envision that there will be a time when personalized MT engines become a feasible option for each linguist, who will give purposes and meanings to these MT “translators,” guiding them to better represent human experiences. Humans will be focusing on the coordination and the leadership role in an MT implementation process. Ultimately it is humans who are the end-users of technology, be it human-guided tools or machine learning tools.  Machines represent extended intelligence of humans. They are about humans, about every one of us. That is the reason why we are drawn to them.  

MT suppliers: how can you meet your client’s needs effectively?  

For an MT supplier, it is not surprising that a client asks you many questions that you might feel are outside of the territory of machine translation, as MT systems integrate aspects of other language technologies, such as segmenting, terminology, and translation memory. While these could be exciting new business opportunities, an MT supplier should be able to understand client needs and decide on your level of  involvement based on your own business priorities and resources, from just offering MT  interface, methodology and technological support, to MT customization, and to offering  the whole package including MT engines, linguists and QA services.  

No matter what, a key foundation is to build trust with your collaborators. For many people, an MT engine is like a black box and what usually matters to other human players, including clients, project managers and linguists, is transparency and fairness.  Working in a machine learning environment often means the same resources will be shared by both humans and machines. MT deployment can be machine oriented or human oriented. Having a fair and transparent approach will help all stakeholders plan for both human learning and machine learning.  

Takeaways:  

  1.  Traditional localization workflow will be updated with MT as a new player 
  2.  MT systems help human translators explore their internal language model
  3.  MT suppliers need to build trust with other human players

If you want to know more about machine translation, sign up for our next Machine Translation Master Class.

 

Learn More:

 

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If you are interested in learning more about the Machine Translation Master Class please click here.
Peng

About the Author

Dr. Peng Wang has taught, researched and practiced translation and localization on three continents. She is the convener for EDUinLOC, the chair of the automation/AI track for LocWorld conferences and a part-time professor at the University of Ottawa. Previously, she was the CAT Tools Coordinator at the University of Maryland.

Dr. Wang has rich research experience on NLP AI, having worked as a linguistic researcher at the Corpus Research Lab at Northern Arizona University, a domain expert for data mining projects at the University of Maryland and an honorary research fellow on automatic discourse analysis tools at the University of Liverpool. Her current research and practice focus on human learning vs. machine learning, machine translation, terminology and multilingual corpus analysis.

Dr. Wang is an expert in approaching technology in the context of culture and humanities. She embraces linguistic and cultural diversity in her classrooms, with students aged from 18 to over 70, in over 10 language combinations, coming from UAE, China, Italy, Spain, Germany, Morocco, Colombia, Mexico, and Haiti, to name just a few.

 

Connect with Dr. Peng Wang:

Connect with Dr. Peng Wang on LinkedIn: https://www.linkedin.com/in/pengjanewang/.

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