On the Evaluation of Semantic Phenomena inNeural Machine Translation Using Natural Language Inference
Cuadro 5: Accuracies for MNLI test sets. MNLI-1 refers to the matched case and MNLI-2 is the mismatched.
['[EMPTY]', 'ar', 'es', 'zh', 'de', 'MAJ']
Though our main focus is exploring what NMT encoders learn about distinct semantic phenomena, we would like to know how useful NMT models are for general NLI across multiple domains. Therefore, we also evaluate the sentence representations with Multi-NLI. However, our results are not competitive with state-of-the-art systems trained specifically for Multi-NLI Nangia et al.