Cross-lingual Word Segmentation and Morpheme Segmentation as Sequence Labelling
Table 1: Hyper-parameters for segmentation.
['Character vector size', '50']
We use one set of parameters for both tasks on all the provided data sets. The weights of the neural networks, including the character vectors, are initialised using the scheme introduced in \newciteglorot2010understanding. The network is trained with the error back-propagation algorithm. The vector representations of input characters are fine-tuned during training by back-propagating gradients. Adagrad Duchi et al. mini-batches is employed for optimisation with the initial learning rate η0=0.1, which is updated with a decay rate ρ=0.05 as ηt=η0ρ(t−1)+1, where t is the index of the current epoch. To increase the efficiency and reduce memory demand both for training and decoding, we chop the sentences longer than 300 characters. For decoding, the chopped sentences are recovered after being processed.