Two different forms of embeddings are used based on the word form, one being obtained by using a randomly initialized look-up table E w , 2 We remove the illegal table-filling labels during decoding for training and testing. The above two components have also been used by <cite>Miwa and Bansal (2016)</cite> . We further enhance the word representation by using its character sequence Lample et al., 2016) , taking a convolution neural network (CNN) to derive a character-based word representation h char , which has been demonstrated effective for several NLP tasks (dos Santos and Gatti, 2014) .