Relational Sentence Embedding for Flexible Semantic Matching

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 tar-get 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. 1