Adaptative Bilingual Aligning Using Multilingual Sentence Embedding
Olivier Kraif
In this paper, we present an adaptive bitextual alignment system called
AIlign. This aligner relies on sentence embeddings to extract reliable anchor
points that can guide the alignment path, even for texts whose parallelism is
fragmentary and not strictly monotonic. In an experiment on several datasets,
we show that AIlign achieves results equivalent to the state of the art, with
quasi-linear complexity. In addition, AIlign is able to handle texts whose
parallelism and monotonicity properties are only satisfied locally, unlike
recent systems such as Vecalign or Bertalign.