We present Relational Sentence Embedding (RSE), a new paradigm to further
discover the potential of sentence embeddings. Prior work mainly models the
similarity between sentences based on their embedding distance. Because of the
complex semantic meanings conveyed, sentence pairs can have various relation
types, including but not limited to entailment, paraphrasing, and
question-answer. It poses challenges to existing embedding methods to capture
such relational information. We handle the problem by learning associated
relational embeddings. Specifically, a relation-wise translation operation is
applied to the source sentence to infer the corresponding target sentence with
a pre-trained Siamese-based encoder. The fine-grained relational similarity
scores can be computed from learned embeddings. We benchmark our method on 19
datasets covering a wide range of tasks, including semantic textual similarity,
transfer, and domain-specific tasks. Experimental results show that our method
is effective and flexible in modeling sentence relations and outperforms a
series of state-of-the-art sentence embedding methods.
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研究了建立新的 Relational Sentence Embedding（RSE）范式以更好地发现句子嵌入潜力，提出了一种关联嵌入学习方法，作用在源句子上，推断出使用现有的 Siamese-based encoder 的对应目标句子，从学习的嵌入中可以计算出细粒度的关系相似度得分。该方法在 19 个任务中进行了基准测试，取得了良好的效果。