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layout: post
title: Detecting Machine Translation
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Code and dataset available on the project [Github](https://github.com/Joao-M-Almeida/JEECUnbabelChallenge).

---
layout: post
title: Detecting Machine Translation
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### TL;DR
 I and Ricardo Martins decided to participate on the JEEC/Unbabel Machine Translation Detection Challenge, we ended in 4th place. The Code and dataset are available on the project [Github](https://github.com/Joao-M-Almeida/JEECUnbabelChallenge).

### The Challenge


At the [JEEC16](http://jeec.tecnico.pt/jeec16/) (a Electrical and Computer Engineering event organized by the students at [Técnico](http://tecnico.ulisboa.pt)) conference [Unbabel](https://unbabel.com/) (a Portuguese startup that does human powered machine translation) went to give a talk and proposed a challenge to the students attending. I and Ricardo Martins decided to participate.

The __goal__ of the challenge was, given a dataset, to create a model that would distinguish between human translated and machine translated.


### Final Results:
  Voting Classifier based on LDA, LogisticRegression and AdaBoost with Dimensionality reduction by Pricinpal component analysis achieved a cross validation score of (55.0 +/- 1.6)% and a final result of 57.8%.

#### Global Results:
1. Francisco Dias: 59.72%
2. Catarina Silva: 59.22%
3. Miguel Borges Ribeiro, Tiago Baltazar: 58.39%
4. __João Almeida, Ricardo Martins: 57.80%__
5. João Rocha e Melo, Miguel Monteiro: 56.34%
6. António Lopes: 55.62%
7. Tiago Santos, Nuno Xu: 51.77%
8. Bruno Henriques, Joana Lapas: 50.25%
9. Sandro Nunes: 47.86%
10. Gonçalo Correia: 47.61%
11. Ricardo Amendoeira: 45.56%
12. Jorge Matos: 40.43%
13. Luis Novoa, Maria Carvalho: 0.00% (file missing results)

Work on the Unbabel post
Work on the Unbabel post
