Acknowledgements
Table 3.3: The accuracy (in %) of word embeddings on the word similarity testsets. The original testset (Mikolov et al., 2013) does not contain many OOV words, thus the score cannot be computed. Our testset is constructed in a similar way as the original, although it is more challenging and contains many OOV question pairs.
['Testset', 'Skip-gram All', 'Skip-gram Only  OOV', 'SubGram All', 'SubGram Only  OOV']
By comparing both approaches on the original testset, we see that both algorithms reach overall a similar performance in the syntactic pairs. On the other hand, SubGram does not capture the tested semantic relations at all. BiRNN Bidirectional Recurrent Neural Network BP Brevity Penalty BPE Byte Pair Encoding CNN Convolutional Neural Network IWSLT International Workshop on Spoken Language Translation LanideNN Language Identification by Neural Networks LM Language Model LSTM Long Short-Term Memory cells MT Machine Translation NLP Natural Language Processing NMT Neural Machine Translation NN Neural Network PBMT Phrase-Based Machine Translation POS Part-of-Speech OOV Out-of-Vocabulary RNN Recurrent Neural Network SMT Statistical Machine Translation T2T Tensor2tensor WAT Workshop on Asian Translation WMT Workshop on Statistical Machine Translation