  Document alignment aims to identify pairs of documents in two distinct
languages that are of comparable content or translations of each other. Such
aligned data can be used for a variety of NLP tasks from training cross-lingual
representations to mining parallel data for machine translation. In this paper
we develop an unsupervised scoring function that leverages cross-lingual
sentence embeddings to compute the semantic distance between documents in
different languages. These semantic distances are then used to guide a document
alignment algorithm to properly pair cross-lingual web documents across a
variety of low, mid, and high-resource language pairs. Recognizing that our
proposed scoring function and other state of the art methods are
computationally intractable for long web documents, we utilize a more tractable
greedy algorithm that performs comparably. We experimentally demonstrate that
our distance metric performs better alignment than current baselines
outperforming them by 7% on high-resource language pairs, 15% on mid-resource
language pairs, and 22% on low-resource language pairs.
