Title: EIT: Enhanced Interactive Transformer for Sequence Generation. Abstract: In this work, we tackle the head degradation problem in attention. We propose an \textbf{E}nhanced \textbf{I}nteractive \textbf{T}ransformer (\textsc{Eit}) architecture in which the standard multi-head self-attention is replaced with the enhanced multi-head attention (EMHA). EMHA removes the one-to-one mapping constraint among queries and keys in multiple subspaces and allows each query to attend to multiple keys. On top of that, we develop a method to make full use of many-to-many mapping by introducing two interaction models, namely Inner-Subspace Interaction and Cross-Subspace Interaction. Extensive experiments on a wide range of sequence generation tasks (e.g. machine translation, abstractive summarization and grammar correction), show its superiority, with a very modest increase in model size. 