  Computing universal distributed representations of sentences is a fundamental
task in natural language processing. We propose a method to learn such
representations by encoding the suffixes of word sequences in a sentence and
training on the Stanford Natural Language Inference (SNLI) dataset. We
demonstrate the effectiveness of our approach by evaluating it on the SentEval
benchmark, improving on existing approaches on several transfer tasks.
