  Monolingual word alignment is important for studying fine-grained editing
operations (i.e., deletion, addition, and substitution) in text-to-text
generation tasks, such as paraphrase generation, text simplification,
neutralizing biased language, etc. In this paper, we present a novel neural
semi-Markov CRF alignment model, which unifies word and phrase alignments
through variable-length spans. We also create a new benchmark with human
annotations that cover four different text genres to evaluate monolingual word
alignment models in more realistic settings. Experimental results show that our
proposed model outperforms all previous approaches for monolingual word
alignment as well as a competitive QA-based baseline, which was previously only
applied to bilingual data. Our model demonstrates good generalizability to
three out-of-domain datasets and shows great utility in two downstream
applications: automatic text simplification and sentence pair classification
tasks.
