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Tag Archives: Machine Translation

Machine Translation Master Course Newsletter – Issue 5

Machine Translation Master Course Newsletter – Issue 5

Machine Translation Master Class Newsletter – Issue 5 – January 2022

 

Since the machine translation master class was launched in October 2020, students from all over the world have taken it. As the instructor, I thoroughly enjoyed working with them and humbly collected their feedbacks along the way: (1) going a bit more into practicalities of existing MT solutions; (2) balancing the content to meet the needs of class participants with various backgrounds; and (3) encouraging conversation with the other course participants. Their valuable suggestions are the reason why we are making some changes in 2022.

 

Feedback #1: Rethink Operational Practices

 

To address feedback #1, the second session will emphasize MT implementation based on knowledge of the inner workings of multiple MT paradigms. Then session 4 will revisit this topic from the angle of risk management. It focuses on humans’ role to leverage various resources for translation quality control, as well as the coordination between MT and its supporting techniques.

In an MT implementation process, buyers and suppliers have different considerations. However, the boundaries become more blurry due to the impact of AI. For example, specialized suppliers, typically those technology companies, start to expand their business scope from simply offering tools to a complete solution that includes both manpower and technologies. Traditional language service providers are developing their own MT systems to be more competitive. We will incorporate this dynamic feature when discussing use cases and the practicalities of MT solutions in our class.

 

Feedback #2: Stakeholder Collaboration

 

Class participants have various backgrounds, being primarily a translator, a project manager, a localization architect, and a decision maker, to name just a few. To balance the teaching content for them is a challenge, yet when we consider the collaboration among stakeholders, this task is manageable. After all, no matter what role one plays in a localization process, they are all humans.

Naturally, early progress in MT started in the technical world, who focused on sketching out the mathematical framework to tackle the problem of translation in their own way. The famous quote by Frederick Jelinek in 1998, “Every time I fire a linguist, the performance of the speech recognizer goes up” showed that this approach worked two decades ago. However, in the new era of AI, simply relying on quantity alone to get the low hanging fruits no longer applies to harder, more subjective problems. In particular, in the MT deployment stage, what really makes a difference is human users, who give meaning and purpose to it by customizing and designing it. Collaboration among people with different roles is crucial for MT deployment. This class will focus on the common ground of MT implementation shared by stakeholders, including the technological principles behind MT, methods for quality control as well as Do’s and Don’ts in specific use scenarios.

 

Feedback #3: Prioritize Human Learning

 

Feedback # 3 is absolutely valuable. Learning is, really, to find out what we already know and to explore what we can know. To better help participants learn, we will implement a new class format, that is, each session will have a 10 minute break between two 45-minute periods, followed by 20 minutes of Q&A. In addition, we will use an e-learning platform to facilitate communication among class participants as well as that between participants and the instructor.

I firmly believe that the fundamental solution for practitioners seeking to leverage AI is that humans have better learning capabilities than machines. In other words, we will feel confident in using it only when we know we can beat computers in learning. Even though many aspects of machine learning in translation will be covered, human learning will and should be prioritized in our class.

 

Looking forward to seeing you at the Machine Translation Master Class, 2022 edition – you can sign up here!

 

 PostScript:

Some might wonder how the Machine Translation Master Class is different from another class I am offering in March 2022: Ready-Set-Go THINK NLP. If so, please check out the following table to compare their features:

 

Machine Translation Master Class Think NLP RSG class
In common Both classes are on technology, but prioritize humans. They approach translation and localization from the perspective of a linguist, a project manager and other domain expert, as opposed to a technical expert such as a computer programmer, both of whom are main drivers leveraging AI to magnify and scale human knowledge of translation.
Differences ●       It focuses on machine translation as well as other tasks associated with MT implementation.

●       It includes both the conventional CAT tool environment and innovations MT can bring about.

●       It emphasizes humans’ role in MT quality evaluation, estimation and overall quality control.

●       It does not prioritize any particular application, but tells the story of neural network language models.

●       It includes multiple NLP tasks that have been or can be applied in a localization process.

●       It depicts the role of localizers in data collection, annotation, management and application.

Key Takeaways ●       Knowledge of multiple MT paradigms

●       A perception of what innovations MT could bring to a CAT tool environment

●       An understanding of humans’ role in MT quality evaluation, estimation and overall quality control

●       An understanding of how to coordinate between MT and its supporting techniques

●       Fundamental NLP AI knowledge and methodologies

●       An understanding of how to get insights from data and metadata as well as a localizer’s role in this process

●       A NLP mindset to help you think proactively to leverage NLP AI in your business

●       A perception of how to effectively motivate and manage your man power in an AI driven localization process

 

 

Learn More:

 

Mach-Trans-MC-rz

 

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

Dr. Peng Wang’s Previous Newsletters

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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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 4

Machine Translation Master Course Newsletter – Issue 4

Machine Translation Master Class Newsletter – Issue 4

 

Recently, Alex Bernet, Manager of Master Class Programming at The Localization Institute interviewed me as the instructor for MTMC. In the interview, I talked about a holistic approach to examining machine translation, human purposes as well as the relationship between CAT and MT. Here I will further elaborate these points. 

 

Intentionality – what distinguishes between humans and machines

 

Imagine yourself alone in a room following a computer program for responding to Chinese characters slipped under the door. You understand nothing of Chinese, and yet, by following the program for manipulating symbols and numerals just as a computer does, you send appropriate strings of Chinese characters back out under the door, and this leads those outside to mistakenly suppose there is a Chinese speaker in the room. This is the famous “Chinese Room Argument” experiment proposed by John Searle in 1980. The person in this experiment is simply an instantiation of a computer program. 

But it does not have intentionality. Such intentionality (as computers appear to have) is solely in the minds of those who program them and those who use them, those who send in the input and those who interpret the output. A computer and its programs are unintentional in nature. That is not their weakness; that is exactly the reason why we use them. They are applicable and potentially useful for any particular person who is willing to and able to bring them to life through the construction of intentionality in his mind. In this perspective, computers and their programs are, no matter whether they are a “learning machine” or not, just tools for human beings – no more, no less. 

 

From CAT to MT – expansion of the technological landscape 

 

The language industry never stops its efforts in using technologies to improve the productivity and efficiency of translation. In the past few decades, it has made great strides in leveraging machine power in a localization workflow. To begin with, computer assisted translation (CAT) tools are used to separate translatable content from formatting, segment and prepare the content in a manner that helps human translators focus on the actual translation. CAT leads to a great success of the accumulation of more relevant language data. And with exponential growth in computing power, the industry starts to revisit the possibility of machine translation and seek the feasibility to ask machines to do more, aiming not only to assist humans to translate, but to translate. 

Any technology can be viewed from two angles: that from a developer’s point of view and that from a user’s. From a developer’s angle, CAT aims to assist human translators whereas MT to train machines for better accuracy and fluency. From a user’s perspective, however, both CAT and MT are used to help humans realize their goals.  

 

A holistic view: MT as part of the human translation process 

MT and CAT are not two separate states. Rather, they are a natural continuation of human’s efforts to use technology. The rise of MT is also a manifestation of the success of CAT tools.  

Likewise, machine translation and human translation are not mutually exclusive. From a user’s perspective, both humans and machines are needed in each phase of a translation process, including the analysis of the source text, parsing of the information, terminology management, the actual language conversion, reviewing and quality evaluation, and publishing. At each phase of the localization process, MT and its relevant technologies can provide insights for humans to draw on. 

The revolution is under way already. The key to meet potential challenges during this journey is to empower users –  translators, project managers, clients and other stakeholders of the localization process – with the most relevant knowledge, skills and best practices so that they can communicate their intentions to machines. By taking a holistic approach to examining machine translation and focusing on the principles behind MT as well as its deployment in various business scenarios, we are getting to the root of the problem.  

 

Takeaways:

  1. Intentionality as computers appear to have is solely in the minds of those who program them and those who use them.
  2. Computers and their programs are essentially tools for human beings.
  3. MT and CAT are not two separate states. 

 

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

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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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 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:

 

Mach-Trans-MC-rz

 

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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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

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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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

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/.

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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