1. Machine Learning, Language Rules, and Statistical Strategies for Language Translation
Andrew Runge
Computer Systems Lab

2. Abstract Goal: Create an efficient, accurate Latin translator Methods:
Language Rules
Machine Learning
Statistical Translation
Language: Python

3. Introduction Language Translators Rule-based Strategies
Machine Learning
N-grams
Statistical Translation

4. Background N-grams in Statistical Translation Generating theses
Word tagging
Tagging for word class, case, etc.
Machine Learning

5. Figure 1: Tree of words sorted by sentence role from the
assorted works of Cicero generated by the methods of
McMahon and Smith

6. Discussion Dictionary creation Dictionary Keys and values
Initial work on Machine Learning
Word Tagging

7. Goals Second Quarter Tagging Initial Translation Third Quarter
Continued Translation
Apply Statistical Strategies

8. Figure 2: Demonstration of n-gram generation for determining
word order in a sentence. Generated by Chen et al.

9. Results Current Results: Read from the dictionary
VERY BASIC TRANSLATIONS
Ex: curro ab puella => run by girl