Assessing the Similarity of Cross-Lingual Seq2Seq Sentence Embeddings Using Low-Resource Spectral Clustering

In this work, we study the cross-lingual distance of machine translations through alignment of seq2seq representations over small corpora. First, we use the M2M100 model to collect sentence-level representations of The Book of Revelation in several languages. We then perform unsupervised manifold alignment (spectral clustering) between these collections of embeddings. As verses between translations are not necessarily aligned, our procedure falls under the challenging, but more realistic non-correspondence regime. The cost function associated with each alignment is used to rank the relative (machine) similarity of one language to another. We then perform correspondent alignment over another cluster of languages, this time using FLORES+ parallel NLLB model embeddings. Our experiments demonstrate that the representations of closely-related languages group closely, and are cheap to align (requiring $<$1000 sentences) via our strategy.