  Monolingual word alignment is crucial to model semantic interactions between
sentences. In particular, null alignment, a phenomenon in which words have no
corresponding counterparts, is pervasive and critical in handling semantically
divergent sentences. Identification of null alignment is useful on its own to
reason about the semantic similarity of sentences by indicating there exists
information inequality. To achieve unbalanced word alignment that values both
alignment and null alignment, this study shows that the family of optimal
transport (OT), i.e., balanced, partial, and unbalanced OT, are natural and
powerful approaches even without tailor-made techniques. Our extensive
experiments covering unsupervised and supervised settings indicate that our
generic OT-based alignment methods are competitive against the
state-of-the-arts specially designed for word alignment, remarkably on
challenging datasets with high null alignment frequencies.
