Dialogue summarization has recently garnered significant attention due to its wide range of applications. However, existing methods for summarizing dialogues are suboptimal because they do not take into account the inherent structure of dialogue and rely heavily on labeled data, which can lead to poor performance in new domains. In this work, we propose DIONYSUS (dynamic input optimization in pre-training for dialogue summarization), a pre-trained encoder-decoder model for summarizing dialogues in any new domain. To pre-train DIONYSUS, we create two pseudo summaries for each dialogue example: one is produced by a fine-tuned summarization model, and the other is a collection of dialogue turns that convey important information. We then choose one of these pseudo summaries based on the difference in information distribution across different types of dialogues. This selected pseudo summary serves as the objective for pre-training DIONYSUS using a self-supervised approach on a large dialogue corpus. Our experiments show that DIONYSUS outperforms existing methods on six datasets, as demonstrated by its ROUGE scores in zero-shot and few-shot settings.

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

本文介绍了Z-Code ++，这是一种针对抽象文本摘要优化的新的预训练的语言模型。该模型使用三种技术扩展了艺术编码器模型的状态。首先，我们使用两阶段的预训练过程来改善模型在低资源摘要任务上的性能。该模型首先是使用文本语料库进行语言理解的预先培训的，然后在汇总语料库中不断预先培训，以进行基础文本生成。其次，我们用分离的注意力层代替编码器中的自我发项层，其中每个单词都使用两个向量分别代表其内容和位置。第三，我们使用融合编码器，这是一种以层次方式编码长序列的简单而有效的方法。 Z-Code ++在13个文本摘要任务中的9个跨5种语言中创建了新的艺术状态。我们的模型的参数有效，因为它的表现优于XSUM上600倍较大的Palm-540b，并且在Samsum上的易经的200倍GPT3-175B较大。在零射击和少量设置中，我们的模型大大优于竞争模型。

脑小血管疾病的成像标记提供了有关脑部健康的宝贵信息，但是它们的手动评估既耗时又受到实质性内部和间际变异性的阻碍。自动化评级可能受益于生物医学研究以及临床评估，但是现有算法的诊断可靠性尚不清楚。在这里，我们介绍了\ textIt {血管病变检测和分割}（\ textit {v textit {where valdo？}）挑战，该挑战是在国际医学图像计算和计算机辅助干预措施（MICCAI）的卫星事件中运行的挑战（MICCAI） 2021.这一挑战旨在促进大脑小血管疾病的小而稀疏成像标记的自动检测和分割方法的开发，即周围空间扩大（EPVS）（任务1），脑微粒（任务2）和预先塑造的鞋类血管起源（任务3），同时利用弱和嘈杂的标签。总体而言，有12个团队参与了针对一个或多个任务的解决方案的挑战（任务1 -EPVS 4，任务2 -Microbleeds的9个，任务3 -lacunes的6个）。多方数据都用于培训和评估。结果表明，整个团队和跨任务的性能都有很大的差异，对于任务1- EPV和任务2-微型微型且对任务3 -lacunes尚无实际的结果，其结果尤其有望。它还强调了可能阻止个人级别使用的情况的性能不一致，同时仍证明在人群层面上有用。

我们介绍了Godel（接地开放对话语言模型），这是对话框的大型预训练的语言模型。与诸如Dialogpt之类的早期模型相比，Godel利用了一个新的扎根预训练阶段，旨在更好地支持将Godel适应广泛的下游对话框任务，这些任务需要当前对话外部的信息（例如，数据库或文档）到产生良好的回应。针对一系列基准测试的实验，这些基准涵盖了面向任务的对话框，对话质量质量检查和接地的开放式对话框，表明Godel在几次以上的微调设置中优于最先进的预训练的对话模型，就人类和自动评估。我们评估方法的一个新颖特征是引入了一个效用概念，该概念除了其交流特征（内在评估）外，还评估了响应的有用性（外部评估）。我们表明，外部评估提供了改进的通道间一致性和与自动指标的相关性。代码和数据处理脚本公开可用。

今天的大部分AI系统都专注于使用自我关注机制和变压器架构在大量多样化的数据中实现令人印象深刻的性能收益。在本文中，我们建议使用外部注意机制增强变压器架构，以带来外部知识和背景。通过将外部信息集成到预测过程中，我们希望减少对更大的模型的需求，并增加AI系统的民主化。我们发现所提出的外部注意机制可以显着提高现有AI系统的性能，使从业者可以轻松地将基础AI模型自定义到许多不同的下游应用程序。特别是，我们专注于勤杂朗语推理的任务，展示所提出的外部注意机制可以增加现有的变压器模型，并显着提高模型的推理能力。拟议的系统，知识外部关注推理（Kear），达到了开放的铜商QA研究基准的人类奇偶校验，其准确性为89.4 \％，与人类准确性为88.9 \％。

本文提出了一种新的预先接受训练的语言模型Debertav3，它通过用更换的令牌检测（RTD）更换掩模语言建模（MLM）来改善原始的Deberta模型，更高的预训练任务。我们的分析表明，Vanilla嵌入了电力中的共享损害培训效率和模型性能。这是因为鉴别器的培训损失和发电机的销售损失在不同的方向上拉动令牌嵌入，从而创造“拔河”动态。因此，我们提出了一种新的梯度 - 解开嵌入共享方法，避免了战争动态，提高了训练效率和预训练模型的质量。我们使用与Deberta相同的设置预先接受了培训的Debertav3，以展示其在广泛的下游自然语言理解（NLU）任务上的特殊表现。以八个任务为例，Debertav3大型模型以八个任务为例，平均得分为91.37％，杜伯塔省的1.37％和电力1.91％，在模型中设置新的最先进（SOTA）具有类似的结构。此外，我们预先培训了多语思伯类Mdeberta，与英语模型相比，对强基线的更大改善。例如，Mdeberta基地达到XNLI的79.8％零射频精度和超过XLM-R基础的3.6％的改进，在此基准上创建了一个新的Sota。我们在HTTPS://github.com/microsoft/deberta公开提供我们预先接受的模型和推理码。

几乎所有现有的基于面部动作编码系统的数据集包括面部动作单元（AU）强度信息使用A-E级别分层地向强度值注释。然而，面部表情连续变化，并将从一个状态变为另一个状态。因此，将局部面部AU的强度值重新播出以表示整个面部表情的变化更有效，特别是在表达转移和面部动画的领域。我们将Feafa的扩展与重新标记的DISFA数据库相结合，可在HTTPS://www.iiplab.net/feafa+ /现在提供。扩展Feafa（Feafa +）包括来自Feafa和Disfa的150个视频序列，总共230,184帧，使用表达式定量工具手动注释24重新定义AU的浮点强度值。我们还列出了针对构成和自发子集的粗略数值结果，并为AU强度回归任务提供基线比较。

Recent progress in pre-trained neural language models has significantly improved the performance of many natural language processing (NLP) tasks. In this paper we propose a new model architecture DeBERTa (Decoding-enhanced BERT with disentangled attention) that improves the BERT and RoBERTa models using two novel techniques. The first is the disentangled attention mechanism, where each word is represented using two vectors that encode its content and position, respectively, and the attention weights among words are computed using disentangled matrices on their contents and relative positions, respectively. Second, an enhanced mask decoder is used to incorporate absolute positions in the decoding layer to predict the masked tokens in model pre-training. In addition, a new virtual adversarial training method is used for fine-tuning to improve models' generalization. We show that these techniques significantly improve the efficiency of model pre-training and the performance of both natural language understand (NLU) and natural langauge generation (NLG) downstream tasks. Compared to RoBERTa-Large, a DeBERTa model trained on half of the training data performs consistently better on a wide range of NLP tasks, achieving improvements on MNLI by +0.9% (90.2% vs. 91.1%), on SQuAD v2.0 by +2.3% (88.4% vs. 90.7%) and RACE by +3.6% (83.2% vs. 86.8%). Notably, we scale up DeBERTa by training a larger version that consists of 48 Transform layers with 1.5 billion parameters. The significant performance boost makes the single DeBERTa model surpass the human performance on the SuperGLUE benchmark (Wang et al., 2019a) for the first time in terms of macro-average score (89.9 versus 89.8), and the ensemble DeBERTa model sits atop the SuperGLUE leaderboard as of January 6, 2021, outperforming the human baseline by a decent margin (90.3 versus 89.8). The pre-trained DeBERTa models and the source code were released at: https://github.com/microsoft/DeBERTa 1 .

The learning rate warmup heuristic achieves remarkable success in stabilizing training, accelerating convergence and improving generalization for adaptive stochastic optimization algorithms like RMSprop and Adam. Pursuing the theory behind warmup, we identify a problem of the adaptive learning rate -its variance is problematically large in the early stage, and presume warmup works as a variance reduction technique. We provide both empirical and theoretical evidence to verify our hypothesis. We further propose Rectified Adam (RAdam), a novel variant of Adam, by introducing a term to rectify the variance of the adaptive learning rate. Experimental results on image classification, language modeling, and neural machine translation verify our intuition and demonstrate the efficacy and robustness of RAdam. 1 * Work was done during an internship at Microsoft Dynamics 365 AI. † Work was done during an internship at Microsoft Dynamics 365 AI.