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Tag Archives: Machine Translation

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 good can you leverage machine power?  

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  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 good can you meet client needs?  

For an MT supplier, it is not surprising that a client asks you many questions that you  might feel outside of the territory of machine translation, as MT systems integrated  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:

 

Mach-Trans-MC-rz

 

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

About the Author

Dr. Peng Wang has been teaching, researching and practicing localization in three continents. She is the convener for EDUinLOC, a part-time professor for the School of Translation and Interpretation at the University of Ottawa and a freelance conference interpreter with the Translation Bureau of the Canadian government. Before that, she was a CAT Tools Coordinator at the Graduate Studies in Interpreting and Translation at the University of Maryland. She has chaired the automation/AI track for LocWorldWide conferences since 2020. Her current research interests include human learning vs. machine learning, machine translation risk management, terminology and multilingual data analysis.

Dr. Wang began conducting corpus-based translation studies at the University of Liverpool and later she worked in the Corpus Research Lab at the Northern Arizona University. She has a rich experience of teaching multilingual classes, 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. She is an expert in approaching technology in the context of culture and humanities.

 

Connect with Dr. Peng Wang:

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

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Disclaimer
Copyright © 2021 The Localization Institute. All rights reserved. This document and translations of it may be copied and furnished to others, and derivative works that comment on or otherwise explain it or assist in its implementation may be prepared, copied, published, and distributed, in whole or in part, without restriction of any kind, provided that the above copyright notice and this section are included on all such copies and derivative works. However, this document itself may not be modified in any way, including by removing the copyright notice or references to The Localization Institute, without the permission of the copyright owners. This document and the information contained herein is provided on an “AS IS” basis and THE LOCALIZATION INSTITUTE DISCLAIMS ALL WARRANTIES, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO ANY WARRANTY THAT THE USE OF THE INFORMATION HEREIN WILL NOT INFRINGE ANY OWNERSHIP RIGHTS OR ANY IMPLIED WARRANTIES OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE.

Machine Translation Master Course Newsletter – Issue 2

Machine Translation Master Course Newsletter – Issue 2

Machine Translation Master Class Newsletter – Issue 2

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 discuss it from a data management perspective.

Why is data management relevant to me?

 

In 2016, Google said in a statement when it corrected a bug translating “Russia” as “Mordor”, “Google Translate is an automatic translator — it works without the intervention of human translators, using technology instead.” (See here)

 

Indeed Google Translate did not have human translators involved in its translation process. But remember, all natural language data comes from humans in their daily life. You might have heard things like “if you hear it enough, you’ll start to believe it”.  The “illusion of truth” effect also applies to machines. A machine will believe what it has seen after looking for patterns in hundreds of millions of documents. You can, of course, try to fix some problems by manually correcting them. Yet, in many cases, in particular in a neural MT system, it is very difficult for humans to manually hit the quantity and complexity that the hidden layers present and thus some features have to be deleted in order to avoid potentially catastrophic mistakes. Therefore, it makes a lot of sense if we can control the quality and quantity of data before feeding it to a machine. Data management is one of the most effective ways to control MT related risks.

 

Why is some data more relevant than others?

Relevance, first and foremost, is based on the comparison. In a localization process, this comparison often happens between your data and the source text. You can compare them from different perspectives. A translator, for example, usually judges the relevance of their reference materials by searching for concepts, words or knowledge about these words that are similar to those appearing in the source text. If a 100-page document did not include any of these, most probably this translator will give up reading. Humans can make such decisions in a split second. Yet it is a daunting task for machines to simulate this process. So typically an MT engine would diligently scan the whole database and analyze the pattern. If a big percentage of data is irrelevant, it is a waste of computing power and you could not achieve your goal. Of course, in the machine world, language data is processed in a different way. For example, neural MT uses embeddings to capture word meaning whereas statistical MT uses n-gram to process corpora. So we cannot judge data relevance only from a human’s perspective. Yet this analogy helps you get a rough picture based on your intuition.

Who is involved in the process of managing MT-driven data?

While IT professionals can communicate your ideas to machines, it is translators, linguists, project managers, and content managers, who can really make sense out of the data from a human perspective. With effective communication that is based on relevant technological knowledge, you will be able to generate a “collective” insight from your team, other teams outside your department, clients, end-users, and last but not least, your machine. This insight will navigate your attention to meet your needs.

Finally, it is important to point out that there are many more aspects regarding data management in an MT deployment process. For example, data quantity, data generated in an interactive MT or an MTPE (Machine Translation Post Editing) process, and data format. It is definitely an intriguing topic we can further explore.

 

Takeaways:

 

  1. Data management is one of the most effective ways to control MT related risks
  2. Data relevance is key to train an MT engine
  3. Communication helps the team make sense out of the data

 

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

 

Learn More:

 

Mach-Trans-MC-rz

 

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

About the Author

Dr. Peng Wang has been teaching, researching and practicing localization in three continents. She is the convener for EDUinLOC, a part-time professor for the School of Translation and Interpretation at the University of Ottawa and a freelance conference interpreter with the Translation Bureau of the Canadian government. Before that, she was a CAT Tools Coordinator at the Graduate Studies in Interpreting and Translation at the University of Maryland. She has chaired the automation/AI track for LocWorldWide conferences since 2020. Her current research interests include human learning vs. machine learning, machine translation risk management, terminology and multilingual data analysis.

Dr. Wang began conducting corpus-based translation studies at the University of Liverpool and later she worked in the Corpus Research Lab at the Northern Arizona University. She has a rich experience of teaching multilingual classes, 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. She is an expert in approaching technology in the context of culture and humanities.

 

Connect with Dr. Peng Wang:

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

Contact Us - Machine Translation

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Disclaimer
Copyright © 2021 The Localization Institute. All rights reserved. This document and translations of it may be copied and furnished to others, and derivative works that comment on or otherwise explain it or assist in its implementation may be prepared, copied, published, and distributed, in whole or in part, without restriction of any kind, provided that the above copyright notice and this section are included on all such copies and derivative works. However, this document itself may not be modified in any way, including by removing the copyright notice or references to The Localization Institute, without the permission of the copyright owners. This document and the information contained herein is provided on an “AS IS” basis and THE LOCALIZATION INSTITUTE DISCLAIMS ALL WARRANTIES, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO ANY WARRANTY THAT THE USE OF THE INFORMATION HEREIN WILL NOT INFRINGE ANY OWNERSHIP RIGHTS OR ANY IMPLIED WARRANTIES OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE.

Machine Translation Master Course Newsletter – Issue 1

Machine Translation Master Course Newsletter – Issue 1

Machine Translation Master Course Newsletter – Issue 1

As the Instructor for the new Machine Translation Master Class from The Localization Institute, I’ve been passionate about the relationship between humans and machines for many years. Since 2004 when I completed my PhD thesis entitled Harry Potter and its Chinese Translation using corpus linguistic methodology and translation theories, I have constantly been reflecting on how we can convert our thoughts to machine readable representations and make use of machine power. Machine Translation, in particular, Neural Machine Translation, is definitely a fascinating manifestation of human intelligence in machines. Having said that, I would rather say I am a researcher and practitioner on cultures and humanities in the name of machine learning and artificial intelligence, as I consider such models as neural networks are significant attempts to demonstrate or simulate human intelligence whereas my ultimate goal is to work with you to explore what’s deep in our mind and what our common humanity is. After all, machine is part of human, demonstrating the representable and decipherable part of all of us. On the other hand, all the unexpected aspects that machines are not capable of can be considered risks, which will eventually be taken care of by humans. That’s also the reason why we focus on risk management in this Machine Translation Master Course.

How can I manage the risks related to Machine Translation?

All risk management considerations are built on knowledge and experience. To successfully manage MT-related risks, first and foremost, you have to possess basic knowledge about machine translation so that every decision you are going to make is well balanced between your intuition based on your past experience in the industry and sufficient (not necessarily all) knowledge about machine translation and machine learning. This Machine Translation Master Course covers some of this basic knowledge, for example, some fundamental classifications associated with Machine Translation risk management, including:

  • Two basic types of MT-related risks
  • Two basic types of MT-related technology
  • Two primary purposes of using technology

1. How do I classify the risks related to Machine Translation?

There are two types of risks when we implement machine translation systems: intrinsic and extrinsic ones. In order to control extrinsic risks, you have to possess a good knowledge of what relevant intrinsic factors are. Thus it is important for us to understand the basic inner workings of machine translation as well as its supporting technology. In this Machine Translation Master Course, we mainly focus on the architectural designs of three types of MT systems, namely, rule-based MT, statistical MT and neural MT, as well as relevant CAT tools that are directly useful for various Machine Translation deployment solutions.

2. How do I classify the technology related to Machine Translation?

Fundamentally speaking, there are two types of technology: tool-based technology and intelligent technology. Like why we use bicycles, we use tool-based technological tools to help us improve productivity.  Under these circumstances, human intelligence is the key to success. In terms of intelligent technology, on the other hand, humans are more in a position to monitor and correct machine generated results, which in turn supports machine learning and improves artificial intelligence. Does Machine Translation belong to tool-based or intelligent technology? It depends on such factors as how you deploy Machine Translation systems, the relationship between human & machine, and your purpose of using it.

3. Why do we use technology?

Technology can serve both humans and machines. In essence, our ultimate goal is always to have machines serve us better. Yet nowadays we can see more human-machine interaction (HMI) activities have aimed to train machines more than human beings. This poses new opportunities and challenges for us. Do you know that convincing evidence in cognitive science, computer science and learning theories indicates that human brains learn better than any machine… at least for now? So rather than resisting change, it makes more sense for us to empower ourselves to better prepare for the machine-human revolution. After all, it is all up to each one of you!

 

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

 

Learn More:

 

Mach-Trans-MC-rz

 

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

About the Author

Dr. Peng Wang is a part-time professor for the School of Translation and Interpretation at the University of Ottawa and a freelance conference interpreter with the Translation Bureau of the Canadian government. Before that, she was a CAT Tools Coordinator at the Graduate Studies in Interpreting and Translation at the University of Maryland. She was the coach and curator for the automation track for LocWorldWide42. Her current research interests include cognitive interpreting/translation studies and AI, risk management of NMT implementation, terminology and multilingual data analysis.

Dr. Wang began conducting corpus-based translation studies at the University of Liverpool and later she worked in the corpus linguistic program at Northern Arizona University. She has a rich experience of teaching multilingual classes, with students aging from 22 to 75, in over 10 language combinations, coming from UAE, China, Italy, Spain, Germany, Morocco, Colombia, Mexico, and Haiti, to name just a few. She is an expert in approaching technology in the context of culture and common core humanity.

 

Connect with Dr. Peng Wang:

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

Contact Us - Machine Translation

    Please select if you would like to register for our mailing list to receive more articles like this.
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Disclaimer
Copyright © 2021 The Localization Institute. All rights reserved. This document and translations of it may be copied and furnished to others, and derivative works that comment on or otherwise explain it or assist in its implementation may be prepared, copied, published, and distributed, in whole or in part, without restriction of any kind, provided that the above copyright notice and this section are included on all such copies and derivative works. However, this document itself may not be modified in any way, including by removing the copyright notice or references to The Localization Institute, without the permission of the copyright owners. This document and the information contained herein is provided on an “AS IS” basis and THE LOCALIZATION INSTITUTE DISCLAIMS ALL WARRANTIES, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO ANY WARRANTY THAT THE USE OF THE INFORMATION HEREIN WILL NOT INFRINGE ANY OWNERSHIP RIGHTS OR ANY IMPLIED WARRANTIES OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE.

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