Non-Monotonic Sentence Alignment via Semisupervised Learning

Xiaojun Quan, Chunyu Kit, Yan Song

Abstract: This paper studies the problem of nonmonotonic sentence alignment, motivated by the observation that coupled sentences in real bitexts do not necessarily occur monotonically, and proposes a semisupervised learning approach based on two assumptions: (1) sentences with high affinity in one language tend to have their counterparts with similar relatedness in the other; and (2) initial alignment is readily available with existing alignment techniques. They are incorporated as two constraints into a semisupervised learning framework for optimization to produce a globally optimal solution. The evaluation with realworld legal data from a comprehensive legislation corpus shows that while existing alignment algorithms suffer severely from non-monotonicity, this approach can work effectively on both monotonic and non-monotonic data.