论文标题
Dyne：多文件摘要的动态合奏解码
DynE: Dynamic Ensemble Decoding for Multi-Document Summarization
论文作者
论文摘要
顺序到序列（S2S）模型是自然语言处理中广泛工作的基础。但是，某些应用程序，例如多文件摘要，多模式机器翻译以及机器翻译的自动后编辑后，需要将一组多个不同的输入映射到单个输出序列中。最近的工作为这些多输入设置引入了定制体系结构，并开发了可以处理越来越长的输入的模型。但是，特殊模型体系结构的性能受到可用内域培训数据的限制。在这项工作中，我们提出了一种简单的解码方法，该方法将同一模型的多个实例的输出组合在不同输入上。我们提出的方法允许在多输入设置中直接使用针对香草S2S任务的模型。当每个输入都与其他输入具有显着重叠时，这种情况特别好，就像将有关同一事件的新闻文章压缩为单个连贯的摘要时，我们在几个多文档摘要数据集中获得了最先进的结果。
Sequence-to-sequence (s2s) models are the basis for extensive work in natural language processing. However, some applications, such as multi-document summarization, multi-modal machine translation, and the automatic post-editing of machine translation, require mapping a set of multiple distinct inputs into a single output sequence. Recent work has introduced bespoke architectures for these multi-input settings, and developed models which can handle increasingly longer inputs; however, the performance of special model architectures is limited by the available in-domain training data. In this work we propose a simple decoding methodology which ensembles the output of multiple instances of the same model on different inputs. Our proposed approach allows models trained for vanilla s2s tasks to be directly used in multi-input settings. This works particularly well when each of the inputs has significant overlap with the others, as when compressing a cluster of news articles about the same event into a single coherent summary, and we obtain state-of-the-art results on several multi-document summarization datasets.
