1. Kirk Zhang 张可 COO Wiitrans Network, Ltd.
Hello everyone, thanks for coming, my name is Kirk and I’m from Wiitrans.
Kirk Zhang 张可
COO
Wiitrans Network, Ltd.

2. The Future of Better Resource Management Technology and Big Data
Today, I would like to share how technology can help us manage the translator resource in the smarter and easier way. As of today, I think that the translators are still playing the vital role in the localization industry, meaning if we can manage translator resource well, we can do our localization business well.

3. Wiitrans – Professional Human Translation
Translators
Wiitrans
Translator Selection Method ”Handpick – Verify – Evaluate”
Processing System for Entire Project Lifecycle
Languages
WiiCAT/WiiTM
30+ Language Pairs specially with Asian Expertise
Online Collaborative Translation tool
First allow me to say few words about Wiitrans. Wiitrans is a technology-driven and innovative localization company and currently we mainly focus on professional human translation. Every single translator in Wiitrans has to pass through our “Handpick-Verify-Evaluate” translator selection mechanism, and once passed they will be teamed up based on language pair and specific industry. Wiitrans system is a complete system processing entire project lifecycle from initial project preparation stage to the final project sign-off, including file analysis, quotation generation, translator assignment, online translation, online editing, file-cleanup and project delivery. WiiCAT or WiiTM is our online collaborative translation tool that allows multiple translators to work on the same project with TM sharing instantly at the same time. WiiConnector is to connecting enterprise CMS with Wiitrans system to provide the “continues delivery” for the web content. Our final fantasy is to use technology to do 60% of our daily localization work.
Industries
WiiConnector
Subject-matter Translator Teams for Mainstream Industries
Enterprise Web (CMS) Localization Solutions

4. Translation Quality Matters the Most
Internet, Cloud Computing, Big Data
In-house Development Team
Project Workflow Automation
There is no doubt that in the past decade, the Internet, the Cloud Computing, the Big Data have impacted almost every industry, including language services. More and more localization companies are getting involved in the technology development, and almost all of the World Top 100 localization companies have their own in-house development team. Why? Because it is a time for technology to do a big part of our job. For example, Project Workflow Automation, Online Collaborative Translation, Machine Translation, all of them are big deal to us now. But, as a language service provider, no matter how fast the service you can provide to your client or how convenient the service your client can get from you, delivering High Translation Quality to our clients is still the MUST. I hope everyone here today agree with me on this: Translation Quality matters the most forever.
Online Collaborative Translation
Machine Translation

5. How to ensure High Translation Quality?+ =
So, it leads out this old but unavoidable topic “how to ensure the high translation quality”? I’m sure there are millions of answers, and I believe all of them make sense for sure. In Wiitrans, we built up this formula: Appropriate Workflow + Suitable Translator = High Translation Quality. Two main factors here: workflow and translator. Be more specific, appropriate workflow, suitable translator.
Appropriate
Workflow
Suitable Translator
High Quality

6. Appropriate Workflow Translation + Editing + QA + Proofreading
Translation + Proofreading
Translation Only
Machine Translation + Post Editing + Proofreading
TM Leverage (Fuzzy > 75%) + MT (sentence length < 15 words) + Post Editing + Proofreading
Let me use one slide to explain what I mean by Appropriate Workflow. Again, as a language service provider, we know that the standard workflow normally carried out before delivering to the client is Translation + Editing + QA + Proofreading. This workflow works perfect for publishing or marketing material translation, but it requires a significant cost. Depending on the translation purpose as well as the budget, sometimes we need to adapt the workflow to meet requirement, so we have Translation + Editing, Translation + Proofreading or Translation Only. For example, if the translation is used for internal reference only and you don’t want to spend a lot of money on the translation. The Translation Only might be the right workflow for the purpose. For the rush, high volume and low budget project, we may provide Machine Translation + Post Editing + Proofreading. I personally believe, in the future that the most common workflow will be TM Leverage (Fuzzy > 75%, those sentences with high fuzzy rate greater than 75% are pre-translated) + MT (sentence length < 15 words) + PE + Proofreading. Anyway, as we know, all kinds of workflows can be pre-configured in the system and one of the workflows can be carried out automatically depends on project requirement. So ensuring the Appropriate Workflow for each project is the easy part. But finding the most suitable translator for each project every time is important and hard part.

7. Who are the Suitable Translators?
The Best translators?
The Qualified translators?
The Experienced translators?
So, next question will be Who are the suitable translators? The best translators? The qualified translators? The experienced translators? As we know there are dozens of common languages widely used in this world, and there are hundreds of industries for all kinds of business. No one can translate everything. Actually, there is no one can translate all types of content even for one single language pair. Study shows that in localization industry, only half of translators have language-study background and another half from different industry background. So one of the answers for defining “Suitable Translator” could be the translators who have the good language skill as well as relevant industry knowledge. If you work as project manager in the localization company, another answer could be The translators who always work on the projects of the same product line for the same client, because they are familiar with client’s preference, terms, product knowledge, and so on. So, look at these two answers, do they make sense? I think they make perfect sense. But, in order to achieve this, as a project manager or resource coordinator, you need personally know the translator well and can memorize all this kind of information. If you manage dozens of translators alone, it might be possible. But managing hundreds or thousands of translators by one person, this is mission impossible. Even you are the genius and you can do this, but it still cannot satisfy today’s business needs. Why?
The translators who have good language skill as well as relevant industry knowledge.
The translators who always work on the projects of the same product line for the same client.

8. Agile Translation Semantic Based Composite Matching
Let’s set up a scenario to explain: for example, you have a document needs to be translated and completed in next a couple of hours, and you never used language vendor before and you have no idea which vendor is good. What do you do? Google it online “language service provider”, and there are thousands of vendors appear and you need to pick one or two and make the first contact with them, get more information from them and want to get free sample test to verify who can provide the good translation quality and finally decide which one you will use. And then you need to sign the contract because of the first time. By the time you get all these done, time is up. So this is not an option. Alternatively, you google “agile translation”, and many online translation platforms which provide 24/7 translation service are out there. You pick one, register it online and then upload your file online. I’m sure most of the platform can analyze file word count automatically and show the quotation instantly. You confirm the price, fill in the project requirement, pay for the project online and then wait for delivery. Very likely, your translation will be done on time. But if the translation quality is not good, you will never come back, right? So as online translation platform providers, they need to make sure the translation is completed by the most suitable translators. Because of the agile translation, there is no time for project manager or resource coordinator to communicate with the translator and assign project by man power. You see this whole automatic workflow, the key node is “Assigning the Suitable Translator” for the project. I will use one concrete example to illustrate how Wiitrans makes this happen. The technique we developed it’s called Semantic Based Composite Matching. If using this technique to manage your translator resource well, it will not only help for online translation platform scenario, also help for regular localization business scenario.
Semantic Based Composite Matching

9. Translator Properties
In Wiitrans, we consider each translator as an instance and each instance has 3 properties: Basic Property, Experience Property and Corpus Property. Basic property includes Industry Background and language pair which are verified by our verification process. The Experience property includes how many words have been translated by the translator, how is the overall quality, customers served as well as translator’s reputation (e.g. project on-time delivery, translator’s availability). This property data is evaluated by our evaluation process. Among those 3 properties, corpus property is the major property for semantic matching technique. It includes Translation Memory and Glossary, and we call it translator-oriented language asset.

10. Translator-Oriented Language Asset
Let me explain what the Translator-Oriented Language Asset means. For each project that completed by the translator, the project file contains some industry-specific terms, the terminology. The list of those terms is translator-oriented glossary. Similar to it, the translation for the whole file content is translator-oriented TM. The more projects the translator completed, the bigger translator-oriented language asset will be.
Translator-Oriented Glossary
Translator-Oriented TM
Terms contained in the project
Project file content
files that translated by the
translated by the translator.
translator.

11. Translator Resource Management System
Our Translator Resource Management System has 4 parts, Translator Verification, Translator Evaluation, Online Translation and Semantic Matching. I’ll give you detailed Introduction for each, especially for Semantic Matching.

12. Translator Verification
Handpick – Native Speakers, Industry Specific, Experience, Reference Check
Test – Translation Skill Test
Before we enter the translator’s details into the system, we carefully handpick native speaking translators based on their language pair and industry knowledge. First, we check their references, including nationality to clarify their native language; the major studied in college, translation skill certifications if any, what type of content they have worked on, how many words they have completed, and what big name’s project (I mean the company) they have been working for. After handpick phase, the qualified candidates are invited to participate our internal translation skill test. We have specially designed test questions for each language pair and industry and each question contains some industry terms. Also we have prepared the referenced translation stored in the system for each question. The translation skill test is carried out online. When candidate submitted the test, the system will automatically compare the submitted translation with the system referenced translation to retrieve the test score. The automatic test score is based on 3 aspects. The full score is 100, terminology check is the most important aspect so it takes 55 scores, and it indicates how good the translator’s industry knowledge is; Consistency check includes numbers, currency, date as well as translation for repetition (the repeated sentences). It will verify if translator took the translation test seriously or not. Certainly we do not need careless translators. As we know the language description can be various, and there is no single fixed translation for each question, so this part only takes 20 scores. Overall, if score is less than 50, the system will make test failed automatically, if greater than 90, the system will make test passed automatically. Any score in between, system will send it to QA team for manual verification. So, in the real life, we use technology to filter out very bad ones and very good ones, and it saves us a lot of time.

13. Translator Evaluation
Project-Based Ongoing Evaluation
KPI Evaluation for all Projects
KPI Data Record for all Translators
System Entry and Human Entry
To map out the most suitable translators for each project, our translator evaluation is project-based ongoing evaluation, and it is the key component to provide experience property data for the semantic matching process. It monitors all translators’ performance and records their KPI data in our system. Data is kept and evaluated on the basis of translation work that is categorized by translation quality, percentage of on-time delivery, compliance to guidelines, response time, communication skills as well as availability. To make our evaluation more objective, the evaluation data is composed of System entry and Human entry. The system data entry is done automatically, it includes if translation is completed on time, how many valid words the translator has translated in the system, the translator’s availability (for example, how many times the translator rejected the project that assigned to him during the certain period). The human data entry has two parts, one from the Editors and another from the clients.

14. Evaluation from Editors
When the first phase of translation is done online, the translation will be viewed by higher level translator and the translation quality report with the score will be automatically generated by the system. It looks like this. The quality report will be released to the translator who is responsible for the first-phase translation, and it has two purposes: one is that translators can improve their translation skills more quickly and more efficient; and another is that system can record their translation quality and transit this information into the semantic matching module. In the quality report, if any sentence with critical error (e.g., this one with -2 points), then the whole sentence and terms contained in the sentence won’t be counted as translator-oriented corpus data. Because we need to make sure this big data is good and accurate.

15. Evaluation from Clients
When a project is completed, client can provide feedback in multiple dimensions, including translation quality, quick response, proper communication as well as timely delivery. This feedback data from client combined together with translation quality report from editor will be stored as evaluation data for Experience property.

16. WiiCAT Maintains Translator Corpus Data
The semantic matching only works when there is enough corpus data available. It is not possible for human to manually manage corpus data for each translator. That’s why we provide a free online translation tool (WiiCAT) to do the job. Translators can easily use WiiCAT to do the translation work as fuzzy sentences and term translations are automatically recommended on the screen, also the translator can submit the terms to the system while translating online to extend our Master glossary. During the online translation process, all translations made by the translator including sentences and terms will be stored as a translator-oriented corpus data and ready for semantic matching purpose.

17. Semantic Based Composite Matching
In Wiitrans, each translator has three property layers and these property data continuously accumulated from time to time. The longer the translators stay with the system, the more property data the system will get. When the new project comes in, based on project requirements, the first layer determines the scope of the translator pool by language pair and selected industry; the second layer narrows down the translator list based on their experience, again including translation quality, how many words have been translated, customers served as well as the reputation; the third layer ensures the final list of the most suitable translators through the semantic matching process.
For the third layer, as mentioned before, the terms that the translator translated during the past projects are stored as a translator-oriented glossary, and the sentences that the translator translated during the past projects are stored as a translator-oriented TM. What happens here is that when the project file uploaded to the system, the terms in the file are extracted automatically based on our master glossary for the selected industry. What happens then is that the system will use the terms that contained in the project file against each individual translator-oriented glossary to retrieve the glossary matching score. The translator list was filtered by the first and second layer. In a similar way, the system will also make new project file leveraged by each individual translator-oriented TM to get TM matching score. By merging those two scores together, we’ll get semantic matching score. This score will determine the final list of the translators who are the most suitable translators for this project.

18. Wiitrans Goal
So, now let’s recall, we have verification data, we have evaluation data, we have corpus data. Because of this big data, it makes our semantic matching technique possible and useful.
Imagine that, each time, when the translators are always assigned with the file content that they are good at, this will help them build up the confidence; the confidence will ensure translators to provide good translation quality; the good translation quality can satisfy client’s expectation and guarantee continues projects; the continues projects can keep translators staying with the system; the longer translators stay with the system, the more data the system will get; the more data we have, the better semantic matching works. This is a closed-loop, and our goal is to use this closed-loop to build up health localization eco-system.

19. Thank You! Kirk Zhang 张可 WeChat Phone: +86 15640058525
Website:
Company： 我译网科技有限公司 Wiitrans Network, Ltd.
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