  Currently, the biaffine classifier has been attracting attention as a method
to introduce an attention mechanism into the modeling of binary relations. For
instance, in the field of dependency parsing, the Deep Biaffine Parser by Dozat
and Manning has achieved state-of-the-art performance as a graph-based
dependency parser on the English Penn Treebank and CoNLL 2017 shared task. On
the other hand, it is reported that parameter redundancy in the weight matrix
in biaffine classifiers, which has O(n^2) parameters, results in overfitting (n
is the number of dimensions). In this paper, we attempted to reduce the
parameter redundancy by assuming either symmetry or circularity of weight
matrices. In our experiments on the CoNLL 2017 shared task dataset, our model
achieved better or comparable accuracy on most of the treebanks with more than
16% parameter reduction.
