Don't Take It Literally: An Edit-Invariant Sequence Loss for Text Generation
This addresses a key limitation in text generation models for applications with noisy or weakly supervised data, offering a drop-in replacement loss function.
The paper tackles the problem of suboptimal training in neural text generation when target sequences are imperfect, proposing an Edit-Invariant Sequence Loss (EISL) that computes matching loss between target and generated n-grams, and shows significant performance improvements over cross-entropy loss on tasks like machine translation with noise, text style transfer, and non-autoregressive generation.
Neural text generation models are typically trained by maximizing log-likelihood with the sequence cross entropy (CE) loss, which encourages an 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 the 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. EISL is designed to be robust to various noises and edits in the target sequences. Moreover, the EISL computation is essentially an approximate 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 CE loss and other strong baselines on all the tasks. EISL has a simple API that can be used as a drop-in replacement of the CE loss: https://github.com/guangyliu/EISL.