Learn to Compose Syntactic and Semantic Representations Appropriately for Compositional Generalization

Recent studies have shown that sequence-to-sequence (Seq2Seq) models are limited in solving the compositional generalization (CG) tasks, failing to systematically generalize to unseen compositions of seen components. There is mounting evidence that one of the reasons hindering CG is the representation of the encoder uppermost layer is entangled. In other words, the syntactic and semantic representations of sequences are twisted inappropriately. However, most previous studies mainly concentrate on enhancing semantic information at token-level, rather than composing the syntactic and semantic representations of sequences appropriately as humans do. In addition, we consider the representation entanglement problem they found is not comprehensive, and further hypothesize that source keys and values representations passing into different decoder layers are also entangled. Staring from this intuition and inspired by humans' strategies for CG, we propose COMPSITION (Compose Syntactic and Semantic Representations), an extension to Seq2Seq models to learn to compose representations of different encoder layers appropriately for generating different keys and values passing into different decoder layers through introducing a composed layer between the encoder and decoder. COMPSITION achieves competitive and even state-of-the-art results on two realistic benchmarks, which empirically demonstrates the effectiveness of our proposal.