  The ability of transformers to perform precision tasks such as question
answering, Natural Language Inference (NLI) or summarising, have enabled them
to be ranked as one of the best paradigm to address Natural Language Processing
(NLP) tasks. NLI is one of the best scenarios to test these architectures, due
to the knowledge required to understand complex sentences and established
relationships between a hypothesis and a premise. Nevertheless, these models
suffer from incapacity to generalise to other domains or difficulties to face
multilingual and interlingual scenarios. The leading pathway in the literature
to address these issues involve designing and training extremely large
architectures, which leads to unpredictable behaviours and to establish
barriers which impede broad access and fine tuning. In this paper, we propose a
new architecture called Siamese Inter-Lingual Transformer (SILT), to
efficiently align multilingual embeddings for Natural Language Inference,
allowing for unmatched language pairs to be processed. SILT leverages siamese
pre-trained multi-lingual transformers with frozen weights where the two input
sentences attend each other to later be combined through a matrix alignment
method. The experimental results carried out in this paper evidence that SILT
allows to reduce drastically the number of trainable parameters while allowing
for inter-lingual NLI and achieving state-of-the-art performance on common
benchmarks.
  We make our code and dataset available at
https://github.com/jahuerta92/siamese-inter-lingual-transformer.
