Preprocessing contains the loading and preprocessing of the data which is then saved as pickle files - the files containing the vocabularised sentences have already been uploaded to this repository. The files with the pre-trained embeddings were too large to upload and have to be created from Preprocessing.

FFNN contains the Feed Forward Network described in Section 4.1 of the report.

The following models refer to section 4.2 of the report.

GRU_REG uses pretrained embeddings, and splits the data into two batching groups. Each group is then padded with 0 vectors to have the same size. We feed only the last hidden state into the regression network.
GRU_REG_PACKED also uses pretrained embeddings, however we use the pack_padded_sequence functionality for padding. In this file we explored average pooling, max pooling and an attention layer.

The following models refer to section 4.3 of the report.

GRU_REG_EMB uses the same batching methodology as GRU_REG. We learn embeddings for each language seperately.
GRU_REG_EMB_SHARED tries to learn one embedding space for both languages.
GRU_REG_EMB_PACKED uses one embedding space and the pack_padded_sequence functionality for padding.

XLM_FFNN contains the model described in 4.4 Section of the report as well as some necessary preprocessing in order to use pre-trained XLM model as described in Section 3.

BERT_LSTM_FFNN contains the model described in 4.5 Section of the report as well as some necessary preprocessing in order to use pre-trained BERT model as described in Section 3.