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Table 5
Model specifications.
ModelHyperparameters
doclink We set β to be a symmetric vector where each cell βi = 0.01 for all word types of all the languages, and use the MALLET implementation for training (McCallum 2002). To enable consistent comparison, we disable hyperparameter optimization provided in the package. 
 
c-bilda Following the experiment results from Heyman, Vulic, and Moens (2016), we set χ = 2 to make the results more competitive to doclink. The rest of the settings are the same as for doclink
 
softlink We use the document-wise thresholding approach for calculating the transfer distributions. The focus threshold is set to 0.8. The rest of the settings are the same as for doclink
 
voclink We set the scalar β′ = 0.01 for hyperparameter β(r,) from the root to both internal nodes or leaves. For those from internal nodes to leaves, we set β′′ = 100, following the settings in Hu et al. (2014b). 
ModelHyperparameters
doclink We set β to be a symmetric vector where each cell βi = 0.01 for all word types of all the languages, and use the MALLET implementation for training (McCallum 2002). To enable consistent comparison, we disable hyperparameter optimization provided in the package. 
 
c-bilda Following the experiment results from Heyman, Vulic, and Moens (2016), we set χ = 2 to make the results more competitive to doclink. The rest of the settings are the same as for doclink
 
softlink We use the document-wise thresholding approach for calculating the transfer distributions. The focus threshold is set to 0.8. The rest of the settings are the same as for doclink
 
voclink We set the scalar β′ = 0.01 for hyperparameter β(r,) from the root to both internal nodes or leaves. For those from internal nodes to leaves, we set β′′ = 100, following the settings in Hu et al. (2014b). 
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