  Paraphrases are texts that convey the same meaning while using different
words or sentence structures. It can be used as an automatic data augmentation
tool for many Natural Language Processing tasks, especially when dealing with
low-resource languages, where data shortage is a significant problem. To
generate a paraphrase in multilingual settings, previous studies have leveraged
the knowledge from the machine translation field, i.e., forming a paraphrase
through zero-shot machine translation in the same language. Despite good
performance on human evaluation, those methods still require parallel
translation datasets, thus making them inapplicable to languages that do not
have parallel corpora. To mitigate that problem, we proposed the first
unsupervised multilingual paraphrasing model, LAMPAT ($\textbf{L}$ow-rank
$\textbf{A}$daptation for $\textbf{M}$ultilingual $\textbf{P}$araphrasing using
$\textbf{A}$dversarial $\textbf{T}$raining), by which monolingual dataset is
sufficient enough to generate a human-like and diverse sentence. Throughout the
experiments, we found out that our method not only works well for English but
can generalize on unseen languages as well. Data and code are available at
https://github.com/VinAIResearch/LAMPAT.
