A deep Natural Language Inference predictor without language-specific
  training data
Lorenzo Corradi Alessandro Manenti Francesca Del Bonifro Francesco Setti Dario Del Sorbo
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.