Tag: Translation Project Management

VideoLocalize: A Case Study in Innovation

VideoLocalize: A Case Study in Innovation

VideoLocalize is a video localization platform that was developed by the Boffin Language Group to address a well-known challenge in the area of video localization. In its current shape, VideoLocalize integrates a synchronization tool with voice-talent and project management capabilities, allowing the end-to-end management of video localization projects.  It wasn’t conceived of in this way, however, and the journey that the Boffin Language Group undertook under the leadership of its President, George Zhao, is a case study in innovation.

Read full article from here.

How to be a Superhero Vendor Manager?

How to be a Superhero Vendor Manager?

Imagine you receive a new translation request, into your company’s local language. Who would you assign this project to? Write the name of the translator that you would contact first.

That’s the task we all received during my vendor management training over 10 years ago in Rome. There were 15 participants together with me, sitting at a huge U-shaped table. Each of us wrote down one name and then we read it out loud one after another to the whole group. It was easy, we did it quickly and we were all happy we did a good job.

Read full article from here.

What do You Want to See?

What do You Want to See?

Dears,

Thanks for visiting our blog!

Please share with us what do you want to see in our blog during the next period and what are the topics that interest you?

Thanks!

Files, Files Everywhere: The Subtle Power of Translation Alignment

Files, Files Everywhere: The Subtle Power of Translation Alignment

Here’s the basic scenario: you have the translated versions of your documents, but the translation wasn’t performed in a CAT tool and you have to build a translation memory because these documents need to be updated or changed across the languages, you want to retain the existing elements, style and terminology, and you have integrated CAT technology in your processes in the meantime. The solution is a neat piece of language engineering called translation alignment.

Translation alignment is a native feature of most productivity tools for computer-assisted translation, but its application in real life is limited to very specific situations, so even the language professionals rarely have an opportunity to use it. However, these situations do happen once in while and when they do, alignment usually comes as a trusty solution for process optimization. We will take a look at two actual cases to show you what exactly it does.

Example No. 1: A simple case

Project outline:

Three Word documents previously translated to one language, totaling 6000 unweighted words. Two new documents totaling around 2500 words that feature certain elements of the existing files and need to follow the existing style and terminology.

Project execution:

Since the translated documents were properly formatted and there were no layout issues, the alignment process was completed almost instantly. The software was able to segmentize the source files and we matched the translated segments, with some minor tweaking of segmentation. We then built a translation memory from those matched segments and added the new files to the project.

The result:

Thanks to the created translation assets, the final wordcount of the new content was around 1500 and our linguists were able to produce translation in accordance with the previously established style and terminology. The assets were preserved for use on future projects.

Example No.2: An extreme case of multilingual alignment

Project outline:

In one of our projects we had to develop translation assets in four language pairs, totaling roughly 30k words per language. The source materials were expanded with new content totaling about 20k words unweighted and the language assets had to be developed both to retain the existing style and terminology solution and to help the client switch to a new CAT platform.

Project execution:

Unfortunately, there was no workaround for ploughing through dozens of files, but once we organized the materials we could proceed to the alignment phase. Since these files were localized and some parts were even transcreated to match the target cultures, which also included changes in layout and differences in content, we knew that alignment was not going to be fully automated.

This is why native linguists in these languages performed the translation alignment and communicated with the client and the content producer during this phase. While this slowed the process a bit, it ultimately yielded the best results possible.

We then exported the created translation memory in the cross-platform TMXformat that allowed use in different CAT tools and the alignment phase was finished.

The result:

With the TM applied, the weighted volume of new content was around 7k words. Our linguists localized the new materials in accordance with the existing conventions in the new CAT platform and the translation assets were saved for future use.

Wrap up

In both cases, translation alignment enabled us to reduce the volume of the new content for translation and localization and ensure stylistic and lexical consistencywith the previously translated materials. It also provided an additional, real-time quality control and helped our linguists produce a better translation in less time.

Translation alignment is not an everyday operation, but it is good to know that when it is called to deliver the goods, this is exactly what it does.

Reference: https://bit.ly/2p5aYr0

The Augmented Translator

The Augmented Translator

The idea that robots are taking over human jobs is by no means a new one. Over the last century, the automation of tasks has done everything from making a farmer’s job easier with tractors to replacing the need for cashiers with self-serve kiosks. More recently, as machines are getting smarter, discussion has shifted to the topic of robots taking over more skilled positions, namely that of a translator.

A simple search on the question-and-answer site Quora reveals dozens of inquiries on this very issue. While a recent survey shows that AI experts predict that robots will take over the task of translating languages by 2024. Everyone wants to know if they’ll be replaced by a machine and more importantly, when will that happen?

“I’m not worried about it happening in my lifetime” translator, Lizajoy Morales, told me when I asked if she was afraid of losing her job to a machine. This same sentiment echoes with most of Lilt’s users. Of course, this demographic is already using artificial intelligence to their advantage and tend to see the benefits over than the drawbacks.

null

Many translators, however, are quick to argue that certain types of content are impossible to be translated accurately by a machine, such as literature, which relies on a human’s understanding of nuance to capture the author’s intention. Or in fields like legal or medicine, that rely on the accuracy of a human translator.

But even in these highly-specialized fields, machines can find their place in the translation workflow. Not as a replacement, but rather as an assistant. As translators, we can use machines to our advantage, to work better and faster.

But I’m not talking about post-editing of machine translation. In a recent article from a colleague, Greg Rosner talks of the comparison of post-editing to the job of a janitor — just cleaning up a mess. True machine assistance augments the translator’s existing abilities and knowledge, letting them have the freedom to do what they do best — translate — and keeping interference to a minimum.

So how do machines help translators exactly? With an interactive, adaptive machine translation, such as that found in Lilt, the system learns in real-time from human feedback and/or existing translation memory data. This means that as a translator is working, the machine is getting to know their content, style and preferences and thus adapting to this unique translator/content combination. This adaptation allows the system to progressively provide better suggestions to human translators, and higher quality for fully automatic translation. In basic terms, it’s making translators faster and better.

Morales also pointed out another little-known benefit from machine translation suggestions: an increase in creativity. “This is an unexpected and much-appreciated benefit. I do all kinds of translations, from tourism, wine, gastronomy, history, social sciences, financial, legal, technical, marketing, gray literature, even poetry on occasion. And Lilt gives me fantastic and creative suggestions. They don’t always work, of course, but every so often the suggestion is absolutely better than anything I could have come up with on my own without spending precious minutes searching through the thesaurus…once again, saving me time and effort.”

Many are also finding that with increased productivity, comes increased free time. Ever wish there were more hours in the day? If you’re a translator, machine assistance may be the solution.

David Creuze, a freelance translator, told us how he spends his extra time, “I have two young children, and to be able to compress my work time from 6 or 7 hours (a normal day before their birth) to 4 hours a day, without sacrificing quality, is awesome.”

With these types of benefits at our fingertips, we should stop worrying about machines taking the jobs of translators and focus on using the machine to our advantage, to work better and ultimately focus on what we do best: being human.

 

Reference: https://bit.ly/2MgDaAj

What do You Want to See?

What do You Want to See?

Dears,

Thanks for visiting our blog!

Please share with us what do you want to see in our blog during the next period and what are the topics that interest you?

Thanks!

A Beginner’s Guide to Machine Translation

A Beginner’s Guide to Machine Translation

What is Machine Translation?

Machine translation (MT) is automated translation by computer software. MT can be used to translate entire texts without any human input, or can be used alongside human translators. The concept of MT started gaining traction in the early 50s, and has come a long way since. Many used to consider MT an inadequate alternative to human translators, but as the technology has advanced, more and more companies are turning to MT to aid human translators and optimize the localization process.

How Does Machine Translation Work?

Well, that depends on the type of machine translation engine. There are several different kinds of MT software which work in different ways. We will introduce Rule-based, Statistical, and Neural.

Rule-based machine translation (RBMT) is the forefather of MT software. It is based on sets of grammatical and syntactical rules and phraseology of a language. RBMT links the structure of the source segment to the target segment, producing a result based on analysis of the rules of the source and target languages. The rules are developed by linguists and users can add terminology to override the MT and improve the translation quality.

Statistical MT (SMT) started in the age of big data and uses large amounts of existing translated texts and statistical models and algorithms to generate translations. This system relies heavily on available multilingual corpora and an average of two millions words are needed to train the engine for a specific domain – which can be time and resource intensive. When a using domain specific data, SMT can produce good quality translations, especially in the technical, medical, and financial field.

Neural MT (NMT) is a new approach which is built on deep neural networks. There are a variety of network architectures used in NMT but typically, the network can be divided into two components: an encoder which reads the input sentence and generates a representation suitable for translation, and a decoder which generates the actual translation. Words and even whole sentences are represented as vectors of real numbers in NMT. Compared to the previous generation of MT, NMT generates outputs which tend to be more fluent and grammatically accurate. Overall, NMT is a major step in MT quality. However, NMT may slightly lack behind previous approaches when it comes to translating rare words and terminology. Long and/or complex sentences are still an issue even for state-of-the-art NMT systems.

The Pros and Cons of Machine Translation

So now you have a brief understanding of MT – but what does it mean for your translation workflow? How does it benefit you?

  • MT is incredibly fast and can translate thousands of words per minute.
  • It can translate into multiple languages at once which drastically reduces the amount of manpower needed.
  • Implementing MT into your localization process can do the heavy lifting for translators and free up their valuable time, allowing them to focus on the more intricate aspects of translation.
  • MT technology is developing rapidly, and is constantly advancing towards producing higher quality translations and reducing the need for post-editing.

There are many advantages of using MT but we can’t ignore the disadvantages. MT does not always produce perfect translations. Unlike human translators, computers can’t understand context and culture, therefore MT can’t be used to translate anything and everything. Sometimes MT alone is suitable, while others a combination of MT and human translation is best. Sometimes it is not suitable at all. MT is not a one-size-fits-all translation solution.

When Should You Use Machine Translation?

When translating creative or literary content, MT is not a suitable choice. This can also be the case when translating culturally specific-texts. A good rule of thumb is the more complex your content is, the less suitable it is for MT.

For large volumes of content, especially if it has a short turnaround time, MT is very effective. If accuracy is not vital, MT can produce suitable translations at a fraction of the cost. Customer reviews, news monitoring, internal documents, and product descriptions are all good candidates.

Using a combination of MT along with a human translator post-editor opens the doors to a wider variety of suitable content.

Which MT Engine Should You Use?

Not all MT engines are created equal, but there is no specific MT engine for a specific kind of content. Publicly available MT engines are designed to be able to translate most types of content, however, with custom MT engines the training data can be tailored to a specific domain or content types.

Ultimately, choosing an MT engine is a process. You need to choose the kind of content you wish to translate, review security and privacy policies, run tests on text samples, choose post-editors, and several other considerations. The key is to do your research before making a decision. And, if you are using a translation management system (TMS) be sure it is able to support your chosen MT engine.

Using Machine Translation and a Translation Management System

You can use MT on its own, but to get the maximum benefits we suggest integrating it with a TMS. With these technologies integrated, you will be able to leverage additional tools such as translation memories, term bases, and project management features to help streamline and optimize your localization strategy. You will have greater control over your translations, and be able to analyze the effectiveness of your MT engine.

Reference: http://bit.ly/2P85d7P