Kevin Dias from TM-Town presents at the TAUS Tokyo Executive Forum on 9-10 April, 2015

1. TAUS Executive Forum 2015
April 9-10, Tokyo

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

3. 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

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

5. 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

6. 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

7. Dropbox for translators
with added benefits

8. Automatic term extraction
and glossary creation

9. Productivity analysis

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

11. API integration in CAT tools

12. Public profile

13. 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

14. 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

15. 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

16. 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

17. 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

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