Plug-and-play language models (PPLMs) enable topic-conditioned natural language generation by combining large pre-trained generators with attribute models to steer the predicted token distribution towards selected topics. Despite their efficiency, the large amounts of labeled texts required by PPLMs to effectively balance generation fluency and proper conditioning make them unsuitable to low-resource scenarios. We present ETC-NLG, an approach leveraging topic modeling annotations to produce End-to-end Topic-Conditioned Natural Language Generations over emergent topics in unlabeled document collections. We test our method’s effectiveness in a low-resource setting for Italian and perform a comparative evaluation of ETC-NLG for Italian and English using a parallel corpus. Finally, we propose an evaluation method to automatically estimate the conditioning effectiveness from generated utterances.