Document-Level Text Generation with Minimum Bayes Risk Decoding using Optimal Transport

Document-level text generation tasks are known to be more difficult than sentence-level text generation tasks as they require the understanding of longer context to generate highquality texts.In this paper, we investigate the adaption of Minimum Bayes Risk (MBR) decoding for document-level text generation tasks.MBR decoding makes use of a utility function to estimate the output with the highest expected utility from a set of candidate outputs.Although MBR decoding is shown to be effective in a wide range of sentencelevel text generation tasks, its performance on document-level text generation tasks is limited as many of the utility functions are designed for evaluating the utility of sentences.To this end, we propose MBR-OT, a variant of MBR decoding using Wasserstein distance to compute the utility of a document using a sentence-level utility function.The experimental result shows that the performance of MBR-OT outperforms that of the standard MBR in document-level machine translation, text simplification, and dense image captioning tasks.