answer for each dialogue context due to data source limitations. To fulfill the gap between the demand and status quo for one-to-many dataset, we collect a large scale item description corpus from a Chinese e-commerce website to construct the native one-to-many dataset. In this corpus, each item has one description provided by their sellers and multiple recommendation sentences written by third-party who is payed to make these sentences more attractive to customers. The descriptions provided by sellers are usually texts stacking many keywords of the item properties. In the contrary, the recommendation sentences are written according to item descriptions but read more smoothly. For the text generation task, we naturally use the seller’s descriptions as the source to generate multiple recommendation sentences mimicking humans. Since this corpus originates from a real business, texts are of high quality and coherent with sources. Thus it gives rise to a very large and native one-to-many dataset, which is called EGOODS.

After simple cleaning and formatting, EGOODS dataset contains 3001140 source and target pairs from 789582 items in total. So each source item description has 3.8 target recommendation sentences on average. The dataset is split into training/validation/testing parts with respect to items, each of 