A non-projective greedy dependency parser with bidirectional LSTMs
Table 3: bist-covington results on the dev set, for those treebanks that have an official dev set (all treebanks except French-ParTUT, Irish, Galician-TreeGal, Kazakh, Slovenian-SST, Kazakh, Uyghur and Ukrainian). ⋆ indicates the model was also trained with external word embeddings (E). ∙ indicates the bilstm output dimension was 256. The performance of some models is likely to be improved, as its training finished earlier than expected due to lack of time to finish it or memory issues (see also §5)
['[BOLD] Treebank', '[BOLD] Gold treebank LAS  [BOLD] no E', '[BOLD] Gold treebank LAS  [BOLD] E', '[BOLD] Predicted treebank LAS  [BOLD] no E', '[BOLD] Predicted treebank LAS  [BOLD] E']
Embeddings: Word embeddings are set to size 100 and universal tag embeddings to 25. Language-specific tag and morphological feature embeddings are used and set to size 25, if they are available for the treebank at hand. Using external word embeddings seems to be beneficial to improve parsing performance Kiperwasser and Goldberg it also makes models take more time and especially much more memory to train. The external word embeddings used in this work (the ones pretrained by the CoNLL 2017 Due to lack of enough computational resources, we only had time to train 38 models (mainly corresponding to the smallest treebanks) including this information. Adam is used as optimizer Kingma and Ba Models were trained for up to 30 epochs, except for the two smallest training sets (Kazakh and Uyghur), where models were trained for up to 100 epochs. The size of the output of the stacked bilstm was set to 512. For very large treebanks (e.g. Czech or Russian-SyntagRus) or treebanks where sentences are very long (e.g. Arabic), we set it to 256, also to counteract the lack of physical resources to finish the task on time. The number of bilstm layers is set to 2. x=1, y=3, z=1 and v=1). The same is true, except for Chinese (-0.69 decrease in LAS) and Portuguese (-0.09), in the fully predicted configuration (end-to-end parsing). It is easy to conclude from the table that including external word embeddings has a positive effect in most of the treebanks we had time to try. This is especially true when performing end-to-end parsing, where only for three languages (English-LinES, Gothic and Old Church Slavonic) a negative effect was observed.