Title: Don't Take It Literally: An Edit-Invariant Sequence Loss for Text Generation. Abstract: Neural text generation models are typically trained by maximizing log-likelihood with the sequence cross entropy loss, which encourages an \emph{exact} token-by-token match between a target sequence with a generated sequence. Such training objective is sub-optimal when the target sequence is not perfect, e.g., when the target sequence is corrupted with noises, or when only weak sequence supervision is available. To address this challenge, we propose a novel Edit-Invariant Sequence Loss (EISL), which computes the matching loss of a target $n$-gram with all $n$-grams in the generated sequence. Drawing inspirations from the classical convolutional networks (ConvNets) which capture shift-invariance in image modeling, EISL is designed to be robust to the shift of $n$-grams to tolerate various noises and edits in the target sequences. Moreover, the EISL computation is essentially a convolution operation with target $n$-grams as kernels, which is easy to implement and efficient to compute with existing libraries. To demonstrate the effectiveness of EISL, we conduct experiments on a wide range of tasks, including machine translation with noisy target sequences, unsupervised text style transfer with only weak training signals, and non-autoregressive generation with non-predefined generation order. Experimental results show our method significantly outperforms the common cross-entropy loss and other strong baselines on all the tasks. 