论文标题
灵活语义匹配的关系句子嵌入
Relational Sentence Embedding for Flexible Semantic Matching
论文作者
论文摘要
我们提出了嵌入关系句子（RSE），这是一种新的范式，以进一步发现句子嵌入的潜力。先前的工作主要根据句子的嵌入距离对相似性进行建模。由于传达了复杂的语义含义，句子对可以具有各种关系类型，包括但不限于需要，释义和提问。它对现有的嵌入方法提出了挑战，以捕获此类关系信息。我们通过学习相关的关系嵌入来解决问题。具体而言，将关系的翻译操作应用于源句子，以使用预先训练的基于暹罗的编码器来推断相应的目标句子。可以从学习的嵌入中计算细粒的关系相似性分数。我们在19个数据集上基准了我们的方法，其中涵盖了广泛的任务，包括语义文本相似性，传输和特定于领域的任务。实验结果表明，我们的方法在建模句子关系方面具有有效且灵活，并优于一系列最新的句子嵌入方法。 https://github.com/binwang28/rse
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. https://github.com/BinWang28/RSE
