  Sentence semantic matching is one of the fundamental tasks in natural
language processing, which requires an agent to determine the semantic relation
among input sentences. Recently, deep neural networks have achieved impressive
performance in this area, especially BERT. Despite the effectiveness of these
models, most of them treat output labels as meaningless one-hot vectors,
underestimating the semantic information and guidance of relations that these
labels reveal, especially for tasks with a small number of labels. To address
this problem, we propose a Relation of Relation Learning Network (R2-Net) for
sentence semantic matching. Specifically, we first employ BERT to encode the
input sentences from a global perspective. Then a CNN-based encoder is designed
to capture keywords and phrase information from a local perspective. To fully
leverage labels for better relation information extraction, we introduce a
self-supervised relation of relation classification task for guiding R2-Net to
consider more about labels. Meanwhile, a triplet loss is employed to
distinguish the intra-class and inter-class relations in a finer granularity.
Empirical experiments on two sentence semantic matching tasks demonstrate the
superiority of our proposed model. As a byproduct, we have released the codes
to facilitate other researches.
