SufiSent - Universal Sentence Representations Using Suffix Encodings
This addresses a fundamental NLP task for researchers and practitioners, but it appears incremental as it builds on existing methods with specific gains.
The paper tackled the problem of computing universal sentence representations by encoding word suffixes and training on SNLI, achieving improvements on several SentEval transfer tasks.
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.