Symbol embedding methods

Neural network-based symbol (phoneme/character) representation learning techniques that work by applying the distributional hypothesis cross-lingually and simultaneously learning representations for both languages.

Some ways of doing this work better than others. The best method appears to be neural_sixgram2, which is now the only one implemented here. It takes into account a relatively broad context of the symbols, and seems to be fairly robust across language pairs.

• Corruption results
• Learned embedding analysis
• Embeddings from model
• Language-specific embeddings
• Neural sixgram (Xsym) trainer, v1
• Neural sixgram (Xsym) trainer, v2
• Neural sixgram samples prep
• Plots of neural sixgram models
• Store in TSV format
• Validation criterion correlation