1 Introduction
Table 8: Dataset size and Performance of RFC
['[EMPTY]', '[BOLD] Training  [BOLD] Samples', '[BOLD] Test  [BOLD] Samples', '[BOLD] Train  [BOLD] Accuracy', '[BOLD] Test  [BOLD] Accuracy']
We create datasets for each language maintaining the ratio of correct and incorrect translations as 1:1.2. We then created a list of features, explained below, to represent a translation in a 273 length feature vector. Average Vector Similarity: Cosine similarity of average of each word’s vector for each sentence. Similarity Features: We create a cosine similarity matrix using aligned bilingual word embeddings Conneau et al. We borrow the ideas from Gupta et al. We repeat process for ngrams for n=[1,6] by taking the vector average for n>2. n -gram Frequencies: Vector of source and target unigram, bigram and trigram probabilities for each ngram in source and target sentence. Structural Features: Number of words in source sentence and target sentence