TM Town - TAUS Tokyo Forum 2015

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The document summarizes TAUS Executive Forum 2015 which will take place on April 9-10 in Tokyo. It discusses TM-Town's mission to create a better translation world through technology and specialization. Key topics covered include job matching algorithms based on natural language processing of prior work, storing linguistic assets in a "bank", and TM-Town's translation enablement platform including unlimited private storage, term extraction, and job matching based on prior work. Areas for improving existing segmentation and alignment libraries are also mentioned.

TAUS Executive Forum 2015
April 9-10, Tokyo

TM-Town's mission is to create a
better translation world through
technology and specialization.

Job matching based on natural
language processing of prior work
Incoming
translation
job
matching
algorithm
1. work is loaded
2. loaded work can be
leveraged
3. the system "learns"
each translators' areas
of experience
4. better matching

A bank for your linguistic assets
1. Safekeeping
2. Access anywhere
3. Earn interest

For translators
• a place to store and manage your TM's and glossaries, and do cool and useful
things with them
• a place to potentially connect with “spot on” clients simply by allowing your prior
work to speak for itself - without a word of it being disclosed
For translation buyers
For translation companies
• a place to connect with “spot on” translators based on the material you need
translated -- without needing to disclose the work before selection
• as the work is done by specialists, and the matching process is more automated
and more accurate, benefits may be obtained in the areas of quality, price and
turnaround
• a tool to enable your project managers or vendor managers to more quickly, more
easily and more accurately select the best people for new translation jobs

A translation enablement platform
- what’s that?
• Unlimited private storage of TMs & glossaries
• TM & glossary analytics
• Term extraction
• Automatic alignment
• Easy file conversion (TMX, XLIFF, XLS, CSV)
• Ability to share term glossaries
• A powerful API
• Integration with CAT / TEnT tools
• Job matching on the basis of your prior work

Dropbox for translators
with added benefits

Automatic term extraction
and glossary creation

A powerful API
• Easy integration with other CAT tools
• Well-documented
• Public and private endpoints

Privacy
• 3 pieces of metadata are public
1. Language pair(s)
2. Field(s) of expertise you select
3. Number of translation units or term concepts
• The content of any document you upload is
automatically private and secure

Segmentation - why reinvent the
wheel?
• Most segmentation libraries are built to support
only English (or English plus a few other
languages)
• Current solutions do not handle ill-formatted
content well
• Some libraries perform really well when trained
with data in a specific language and a specific
domain, but what happens when your data
could come from any language and/or domain

A comparison of segmentation
libraries
Name Language License
Golden Rule Score !
(English)
Golden Rule Score
(Other Languages)
Speed
Pragmatic Segmenter Ruby MIT 98.08% 100.00% 3.84 s
TactfulTokenizer Ruby GNU GPLv3 65.38% 48.57% 46.32 s
Open NLP Java APLv2 59.62% 45.71% 1.27 s
Stanford CoreNLP Java GNU GPLv3 59.62% 31.43% 0.92 s
Splitta Python APLv2 55.77% 37.14% N/A
Punkt Python APLv2 46.15% 48.57% 1.79 s
SRX English Ruby GNU GPLv3 30.77% 28.57% 6.19 s
Scapel Ruby GNU GPLv3 28.85% 20.00% 0.13 s

Bitext alignment - areas for
improvement
• Early misalignment compounds into
errors throughout
• Accuracy may suﬀer for non-Roman
languages unless the algorithm is
properly tuned
• Does not handle cross alignments nor
uneven alignments

A method for higher accuracy
• Machine translate A - B and B - A
• Relative sentence length
• Order or position in the document
0 1 2 3 4 5
0
1 X
2 X
3
4 X
5 X
X

kevin@tm-town.com
a better translation world through
technology and specialization
Kevin Dias

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Python is a versatile and widely-used high-level programming language known for its simplicity, readability, and extensive library support. Created by Guido van Rossum and first released in 1991, Python has since gained immense popularity across various domains, including web development, data science, scientific computing, artificial intelligence, and more. In this comprehensive description, we'll delve into Python's history, features, applications, and its vibrant community, highlighting why it continues to be a preferred choice for developers worldwide. Table of Contents Introduction to Python Python's History and Evolution Python's Key Features 3.1. Readability and Simplicity 3.2. High-level Language 3.3. Interpreted and Dynamic 3.4. Cross-platform Compatibility 3.5. Rich Standard Library 3.6. Community Support Python's Application Domains 4.1. Web Development 4.2. Data Science and Machine Learning 4.3. Scientific Computing 4.4. Automation and Scripting 4.5. Game Development 4.6. Desktop Applications Python Development Environments 5.1. IDLE 5.2. PyCharm 5.3. Jupyter Notebook 5.4. Visual Studio Code Getting Started with Python 6.1. Installing Python 6.2. Your First Python Program Python Syntax and Basic Concepts 7.1. Variables and Data Types 7.2. Conditional Statements 7.3. Loops 7.4. Functions 7.5. Exception Handling Working with Python Libraries 8.1. NumPy 8.2. Pandas 8.3. Matplotlib 8.4. Scikit-Learn Python and Web Development 9.1. Frameworks (Django, Flask) 9.2. Front-end Integration (HTML/CSS) 9.3. Database Interaction (SQL, NoSQL) Python in Data Science 10.1. Data Analysis with Pandas 10.2. Data Visualization with Matplotlib and Seaborn 10.3. Machine Learning with Scikit-Learn 10.4. Deep Learning with TensorFlow and PyTorch Scientific Computing with Python 11.1. Scientific Libraries (SciPy, SymPy) 11.2. Plotting and Visualization (Matplotlib) Automation and Scripting 12.1. Automating Tasks 12.2. Scripting for System Administration Game Development with Python 13.1. Pygame 13.2. Unity and Unreal Engine Integration Desktop Applications with Python 14.1. Tkinter 14.2. PyQt Python's Ecosystem and Package Management Python Best Practices 16.1. Code Readability (PEP 8) 16.2. Documentation and Comments 16.3. Testing (Unit Testing, pytest) 16.4. Version Control (Git) Python's Future and Trends Conclusion 1. Introduction to Python Python is a general-purpose, high-level programming language that was designed with a focus on code readability and simplicity. It uses an elegant and straightforward syntax that makes it easy for developers to express their ideas effectively, reducing the cost of program maintenance. Python's philosophy emphasizes the importance of code clarity and readability, which is encapsulated in the Zen of Python (PEP 20). The language has gained immense popularity due to its versatility and a rich ecosystem of libraries and frameworks. Python is renowned for its vibrant community and extensive documentation, making it in p

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Translation Technology Showcase in Shenzhen

TAUS - The Language Data Network

The document announces a Translation Technology Showcase event hosted by TAUS on February 28, 2017 in Shenzhen. The event will feature presentations from various translation technology companies on topics like multichannel translation for the digital economy, using free and open source tools, leveraging large translation memories, and neural machine translation. The agenda lists out the scheduled presentations and their times. The document also mentions that TAUS recently published an updated Translation Technology Landscape Report covering trends in the industry and profiles of over 80 companies.

How to efficiently use large-scale TMs in translation, Jing Zhang (Tmxmall)

TAUS - The Language Data Network

Most of LSPs have not converted the translated bilingual documents to TM till now. Even the LSPs have established TMs, they are also confronted with disordered management of TMs and low efficiency. This report will share the way of quick TM establishment with Tmxmall Cloud-Based Smart Aligner, the way of Management of large-scale TMs with Private Cloud-Based TM for achieving pre-translation with large-scale TMs and team cooperation and etc.. Besides, the report will introduce Tmxmall TM marketplace, which is expected to promote TM sharing. Finally, we will share the experience of LSPs on alignment and Private Cloud-Based TM management for reducing translation costs and increasing profits.

SDL Trados Studio 2017, Jocelyn He (SDL)

TAUS - The Language Data Network

SDL is the leader in global content management and language translation solutions. With more than 20 years of experience, SDL helps companies build relevant online experiences that deliver transformative business results on a global scale. Translation Industry continues to grow, and Freelancers, LSPs and Corporate clients all see increased demand as more and more content is created, so we have to address them all. As a Market-leading translation productivity tool, SDL Trados Studio is trusted by over 200,000 translation professionals to boost productivity, control quality and aid collaboration. SDL has launched Trados Studio 2017. This presentation will introduce SDL Trados Studio 2017 and highlight SDL’s new productivity booster- UPLIFT, which is well welcomed by global clients.

How we train post-editors - Yongpeng Wei (Lingosail)

TAUS - The Language Data Network

This document discusses Lingosail's translation technology products and services, including machine translation, corpus construction, and translation services. It outlines how Lingosail's machine translation process editing (MTPE) solutions can provide easier entry into translation for clients, higher translation efficiency, and more scalable management of translation workflows. The document also describes Lingosail's patent post-editing training course for translators, which saw hundreds of participants last year, and resulted in trainees increasing their translation speed and quality after training.

A use-case for getting MT into your company, Kerstin Berns (berns language c...

TAUS - The Language Data Network

This document discusses how to introduce machine translation (MT) into a company to improve localization processes. It outlines challenges with the current process of 30 localization loops involving 40 translators across different locations with no quality or cost control. Introducing MT for display text localization could speed up availability, lower costs by 25%, and reduce unnecessary translation loops by 50%. A short-term goal is to use MT for development phases with a final quality loop involving human translation and post-editing. Long-term preparation is needed to expand MT use while addressing risks, quality guidelines, and system environments.

QE integrated in XTM, by Bob Willans (XTM)

TAUS - The Language Data Network

This document discusses integrating XTM Cloud and TAUS DQF to enable higher quality translation projects. Key steps include creating accounts in both systems, configuring LQA parameters and issues in XTM, creating translation projects in XTM with LQA steps, performing translations and LQA reviews in XTM, and then viewing productivity and quality results in the TAUS DQF system. The integration is meant to provide benefits like higher productivity, improved quality, and better data to evaluate machine translation systems.

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