  Multilingual understanding models (or encoder-based), pre-trained via masked
language modeling, have achieved promising results on many language
understanding tasks (e.g., mBERT). However, these non-autoregressive (NAR)
models still struggle to generate high-quality texts compared with
autoregressive (AR) models. Considering that encoder-based models have the
advantage of efficient generation and self-correction abilities, this paper
explores methods to empower multilingual understanding models the generation
abilities to get a unified model. Specifically, we start from a multilingual
encoder (XLM-R) and propose a \textbf{S}emantic-\textbf{G}uided
\textbf{A}lignment-then-Denoising (SGA) approach to adapt an encoder to a
multilingual generator with a small number of new parameters. Experiments show
that the proposed approach is an effective adaption method, outperforming
widely-used initialization-based methods with gains of 9.4 BLEU on machine
translation, 8.1 Rouge-L on question generation, and 5.5 METEOR on story
generation on XLM-R$_{large}$. On the other hand, we observe that XLM-R is
still inferior to mBART in supervised settings despite better results on
zero-shot settings, indicating that more exploration is required to make
understanding models strong generators.
