  Prompting pre-trained language models has achieved impressive performance on
various NLP tasks, especially in low data regimes. Despite the success of
prompting in monolingual settings, applying prompt-based methods in
multilingual scenarios has been limited to a narrow set of tasks, due to the
high cost of handcrafting multilingual prompts. In this paper, we present the
first work on prompt-based multilingual relation classification (RC), by
introducing an efficient and effective method that constructs prompts from
relation triples and involves only minimal translation for the class labels. We
evaluate its performance in fully supervised, few-shot and zero-shot scenarios,
and analyze its effectiveness across 14 languages, prompt variants, and
English-task training in cross-lingual settings. We find that in both fully
supervised and few-shot scenarios, our prompt method beats competitive
baselines: fine-tuning XLM-R_EM and null prompts. It also outperforms the
random baseline by a large margin in zero-shot experiments. Our method requires
little in-language knowledge and can be used as a strong baseline for similar
multilingual classification tasks.
