Left-corner Methods for Syntactic Modelingwith Universal Structural Constraints
Table 5.6: Attachment scores on Google universal treebanks (up to length 10). All proposed models are trained with the verb-otherwise-noun constraint. Naseem10 = the model with manually crafted syntactic rules between POS tags (Naseem et al., 2010); Grave15 = also relies on the syntactic rules but is trained discriminatively (Grave and Elhadad, 2015).
['[EMPTY]', 'Unif.', '[ITALIC] C=2', '[ITALIC] C=3', '[ITALIC] βlen=0.1', 'Naseem10', 'Grave15']
The scores of the other systems are borrowed from \newcitegrave-elhadad:2015: ACL-IJCNLP. In this experiment, we only focus on the settings where the root word is restricted with the verb-otherwise-noun constraint. Among our structural constraints, again our stack depth constraints perform the best. In particular the scores with C=2 are stable across languages. In this study, we have shown that our imposed stack depth constraint improves the performance of unsupervised grammar induction in many settings. One limitation of the current approach is that the information that the parser can utilize is very superficial (i.e., the first order model on content-head based POS tags). However, our positive results in the current experiment are an important first step for the current line of research and encourage further study on more structurally complex model beyond the simple DMV model.