Multilingual Relation Classification via Efficient and Effective Prompting
This work addresses the challenge of applying prompt-based methods to multilingual NLP tasks, offering an efficient solution for relation classification with minimal in-language knowledge.
The paper tackles multilingual relation classification by introducing a prompt-based method that constructs prompts from relation triples with minimal translation, achieving superior performance over competitive baselines in fully supervised, few-shot, and zero-shot scenarios across 14 languages.
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.