%0 Conference Proceedings %T Massively Multilingual Document Alignment with Cross-lingual Sentence-Mover’s Distance %A El-Kishky, Ahmed %A Guzmán, Francisco %Y Wong, Kam-Fai %Y Knight, Kevin %Y Wu, Hua %S Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing %D 2020 %8 December %I Association for Computational Linguistics %C Suzhou, China %F el-kishky-guzman-2020-massively %X 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. %U https://aclanthology.org/2020.aacl-main.62 %P 616-625