Natural language inference (NLI), the task of recognizing the entailment relationship in sentence pairs, is an actively studied topic serving as a proxy for natural language understanding. Despite the relevance of the task in building conversational agents and improving text classification, machine translation and other NLP tasks, to the best of our knowledge, there is no publicly available NLI corpus for the Romanian language. To this end, we introduce the first Romanian NLI corpus (RoNLI) comprising 58K training sentence pairs, which are obtained via distant supervision, and 6K validation and test sentence pairs, which are manually annotated with the correct labels. We conduct experiments with multiple machine learning methods based on distant learning, ranging from shallow models based on word embeddings to transformer-based neural networks, to establish a set of competitive baselines. Furthermore, we improve on the best model by employing a new curriculum learning strategy based on data cartography. Our dataset and code to reproduce the baselines are available at https://github.com/Eduard6421/RONLI.

翻译：自然语言推理（NLI）作为自然语言理解的代理任务，旨在识别句子对之间的蕴含关系，是一个被广泛研究的活跃课题。尽管该任务对于构建对话代理、改进文本分类、机器翻译及其他自然语言处理任务具有重要意义，但据我们所知，目前尚无公开可用的罗马尼亚语NLI语料库。为此，我们推出了首个罗马尼亚语NLI语料库（RoNLI），其中包含通过远程监督获得的58K个训练句子对，以及人工标注正确标签的6K个验证和测试句子对。我们采用多种基于远程学习的机器学习方法进行了实验，涵盖从基于词嵌入的浅层模型到基于Transformer的神经网络，以建立一组具有竞争力的基线。此外，我们通过采用一种基于数据图谱的新型课程学习策略，进一步提升了最佳模型的性能。我们的数据集及复现基线的代码可在 https://github.com/Eduard6421/RONLI 获取。