Panelists: Yoshiyasu Yamakawa (Intel), JP Barraza (Systran), Konstantin Dranch (Memsource), David Koot (TAUS) The focus of this session will be on predictions and risk management. What kind of things can you predict and how can you manage risks by by analyzing your translation data or monitoring your productivity and quality. Tracking translation data in different cycles of the translation process (translation, post-editing, review, proof-reading) offers tremendous value when it comes to predicting future trends or making informed choices. What type of data can be valuable and what kind of predictions can we make using this data? How can we make more efficient use of already available data? How can we use this type of data to improve machine translation, automatic QA, error-recognition, sampling or quality estimation? How can academia and industry work together towards a common goal?

1. Turning Data into Business
Intelligence
Konstantin Dranch
Memsource

2. Building BI in
a translation
tool
Our approach

3. 1. Separate language data from metadata
visualisation tools API 3rd party BI
Memsource
translation platform
Elastic Search
data warehouse

4. 2. DIY metrics & dashboard builders

5. 3. Embedding data into process

6. 4. Higher data
resolution over
time
1. Project data
2. Job data
3. Segment data
4. Time tracking
5. Subsegment data

7. Thank you!
konstantin@memsource.com

8. Cloud tools lead to Big Data
Server tools: private data silos Cloud tools: centralized data

9. And the clouds are getting bigger…
In May alone, users processed 0.8 billion words in Memsource

11. Benchmarks: translation memory
Data for jobs where post-editing analysis has been performed, December 2015 - May 2016
Sample
9 bn
words

12. Benchmarks: machine translation
Sample size 20 million words, December 2015 - May 2016
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
en:es pt:en en:pt es:en en:ru ru:en en:de pt:es en:fr es:pt
%OFWORDSINSEGEMENTSFROMMT
SAMPLE LANGUAGE PAIRS
EDIT DISTANCE FOR MAJOR LANGUAGE PAIRS
mt.match100 mt.match95 mt.match85 mt.match75 mt.match50 mt.match0
MT not used
Raw MT
Moderate
edits
Heavily
edited

13. Benchmarks: rising languages
% increase Language Volume
67,481.6 Marathi *
3,087.7 Hindi *
1,006.0 Latvian **
784.3 Kazakh *
689.1 Serbian *
659.5 Finnish **
657.5 Romanian *
613.9 Vietnamese *
592.5 Indonesian *
585.7 Turkish **
543.6 Hungarian *
467.4 Italian ***
457.2 Ukrainian *
448.9 Chinese ****
440.7 Swedish **
428.8 Dutch **
390.0 Norwegian *
378.9 English *****
335.2 Danish **
% increase Language Volume
331.3 Estonian *
319.2 Polish **
318.5 Norwegian Bokmål *
307.8 German ***
306.9 Thai *
299.7 Overall increase in volume
295.8 Korean **
269.4 Czech **
268.3 Russian ****
245.5 French ****
241.4 Slovak *
237.8 Portuguese ***
237.4 Bulgarian *
230.0 Arabic *
200.9 Japanese ****
194.8 Spanish ****
137.8 Bulgarian *
121.4 Greek ***
-