The volume of available multilingual data has exploded. One option is to create machine translation systems based on the previously translated segments, with as much data as possible. This works in many cases, but it is well known in the market that the cleaner the data, the better the results in terms of productivity, cost, and even quality. tauyou <language> has made an additional step in the process by optimizing the translation engines on a per-document basis, which has proven to provide a significant quality increase in the machine translation output. This approach, linked to a joint source content optimization and summarization, leads to significant savings in multilingual communications.

1. Optimized
Big Data Approach
to
Machine Translation
Diego Bartolome
@diegobartolome
dbc@tauyou.com

3. Data for Machine Translation

4. Some techniques might work...
Baseline engines
In-domain + out-of-domain balance
Domain-specific engines

5. But ...
Baseline engines
In-domain + out-of-domain balance
Domain-specific engines

7. Big Data Approach
Unilingual
Texts
Small Data
Glossaries/
Dictionaries
Translation
Memories
.
.
.External
Data

8. Average Improvement
+21%

9. Sample Results

10. Important topics
Supervised Data Classification
Data Clustering
Parameter optimization
Key Performance Indicators (KPIs)
Predictive MT quality estimation
Measure + Measure + Measure

12. Not everything that can be counted
counts, and not everything that
counts can be counted.
William Bruce Cameron