In this paper we present a technique of NLP to tackle the problem of inference relation (NLI) between pairs of sentences in a target language of choice without a language-specific training dataset. We exploit a generic translation dataset, manually translated, along with two instances of the same pre-trained model - the first to generate sentence embeddings for the source language, and the second fine-tuned over the target language to mimic the first. This technique is known as Knowledge Distillation. The model has been evaluated over machine translated Stanford NLI test dataset, machine translated Multi-Genre NLI test dataset, and manually translated RTE3-ITA test dataset. We also test the proposed architecture over different tasks to empirically demonstrate the generality of the NLI task. The model has been evaluated over the native Italian ABSITA dataset, on the tasks of Sentiment Analysis, Aspect-Based Sentiment Analysis, and Topic Recognition. We emphasise the generality and exploitability of the Knowledge Distillation technique that outperforms other methodologies based on machine translation, even though the former was not directly trained on the data it was tested over.

翻译：本文提出一种自然语言处理技术，用于在无需特定语言训练数据集的情况下，解决任意目标语言中句子对之间的推理关系（NLI）问题。我们利用一个手动翻译的通用翻译数据集，以及同一预训练模型的两个实例：第一个用于生成源语言的句子嵌入，第二个针对目标语言进行微调以模仿前者。该技术称为知识蒸馏。该模型已在机器翻译的Stanford NLI测试数据集、机器翻译的多体裁NLI测试数据集以及手动翻译的RTE3-ITA测试数据集上进行了评估。我们还在不同任务上测试了所提架构，以实证NLI任务的通用性。具体地，模型在原生意大利语ABSITA数据集上进行了情感分析、基于方面的情感分析和主题识别任务的评估。我们强调知识蒸馏技术的通用性和可开发性，该技术优于其他基于机器翻译的方法，尽管其并未直接在测试数据上进行训练。