There are three Python programs here (-h
for usage):
./default
performs clustering on the training data./baseline
performs k-nearest-neighbor./grade
compares the predicted score with the actual score and returns the accuracy
The first two commands are designed to work in a pipeline with the last. For instance, this is a valid invocation:
./baseline | ./grade
The data-train/
directory contains
+ `es-en_score.train`, which contains the score assigned to each sentence (one score per line)
+ `es-en_source.train`, which contains 1050 Spanish sentences
+ `es-en_target.train`, which contains 1050 English translations
+ `train_features`, which contains the values of each of the 17 features for each sentence
as well as the feature-list
, which contains the list of features being used.
The data-dev/
directory contains:
+ `es-en_score.dev`, which contains the score assigned to every alternate sentence in the test data, i.e. 225 scores. This is what is used to develop locally.
The data-test/
directory contains:
+ `es-en_source.test`, which contains 450 Spanish sentences
+ `es-en_target.test`, which contains 450 English translations
+ `test_features`, which contains the values of each of the 17 features for each test sentence