This paper presents a new sequence-tosequence pre-training model called Prophet-Net, which introduces a novel self-supervised objective named future n-gram prediction and the proposed n-stream self-attention mechanism.Instead of optimizing one-stepahead prediction in the traditional sequenceto-sequence model, the ProphetNet is optimized by n-step ahead prediction that predicts the next n tokens simultaneously based on previous context tokens at each time step. The future n-gram prediction explicitly encourages the model to plan for the future tokens and prevent overfitting on strong local correlations. We pre-train ProphetNet using a base scale dataset (16GB) and a large-scale dataset (160GB), respectively. Then we conduct experiments on CNN/DailyMail, Gigaword, and SQuAD 1.1 benchmarks for abstractive summarization and question generation tasks. Experimental results show that Prophet-Net achieves new state-of-the-art results on all these datasets compared to the models using the same scale pre-training corpus.

In this paper, we introduce XGLUE, a new benchmark dataset that can be used to train large-scale cross-lingual pre-trained models using multilingual and bilingual corpora and evaluate their performance across a diverse set of cross-lingual tasks. Comparing to GLUE (Wang et al., 2019), which is labeled in English for natural language understanding tasks only, XGLUE has two main advantages: (1) it provides 11 diversified tasks that cover both natural language understanding and generation scenarios; (2) for each task, it provides labeled data in multiple languages. We extend a recent cross-lingual pre-trained model Unicoder to cover both understanding and generation tasks, which is evaluated on XGLUE as a strong baseline. We also evaluate the base versions (12-layer) of Multilingual BERT, XLM and XLM-R for comparison. 1

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Trichoderma spp. are versatile beneficial fungi which can stimulate growth and plant resistance to biotic and abiotic stresses. In this study, the potential of Trichoderma isolate in promoting the cucumber growth under salt stress and its possible mechanisms were investigated. Strain Q1 was isolated from the rhizosphere of cucumber in greenhouse in China and identified as Trichoderma asperellum based on its morphological features and the molecular phylogenetic analyses. It exhibited some plant growth‐promoting attributes of phosphate solubilization, 1‐aminocyclopropane‐1‐carboxylate (ACC) deaminase activity, auxin and siderophore production. In pot trials, applying strain Q1 to cucumber plant had significantly promoted seedlings growth and alleviated the growth suppression induced by salt stress as confirmed by the changes in growth phenotype and several biochemical and physiological parameters. In solution culture experiments, the growth of cucumber seedlings was increased and the percentage of wilted cucumber seedlings was decreased in the treatment of siderophore‐containing culture filtrate (SCF) of strain Q1 with insoluble Fe3+ under salt stress. These results indicated that T. asperellum Q1 has a real potential to enhance cucumber growth by inducing physiological protection under saline stress, and its siderophores showed sign of alleviating negative effect of salinity and available iron deficiency.

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Now, the pre-training technique is ubiquitous in natural language processing field. Prophet-Net is a pre-training based natural language generation method which shows powerful performance on English text summarization and question generation tasks. In this paper, we extend ProphetNet into other domains and languages, and present the ProphetNet family pretraining models, named ProphetNet-X, where X can be English, Chinese, Multi-lingual, and so on. We pre-train a cross-lingual generation model ProphetNet-Multi, a Chinese generation model ProphetNet-Zh, two open-domain dialog generation models ProphetNet-Dialog-En and ProphetNet-Dialog-Zh. And also, we provide a PLG (Programming Language Generation) model ProphetNet-Code to show the generation performance besides NLG (Natural Language Generation) tasks. In our experiments, ProphetNet-X models achieve new state-of-the-art performance on 10 benchmarks. All the models of ProphetNet-X share the same model structure, which allows users to easily switch between different models. We make the code and models publicly available 1 , and we will keep updating more pre-training models and finetuning scripts. A video 2 to introduce ProphetNet-X usage is also released.