具有对比性学习目标的预训练方法在对话了解任务中表现出了显着的成功。但是，当前的对比学习仅将自调查的对话样本视为正样本，并将所有其他对话样本视为负面样本，即使在语义上相关的对话框中，也会强制执行不同的表示。在本文中，我们提出了一个树木结构化的预培训对话模型Space-2，该模型从有限标记的对话框和大规模的无标记的对话框COLPORA通过半监督的对比度预培训来学习对话框表示。具体而言，我们首先定义一个通用的语义树结构（STS），以统一不同对话框数据集的注释模式，以便可以利用所有标记数据中存储的丰富结构信息。然后，我们提出了一个新颖的多视图分数功能，以增加共享类似STS的所有可能对话框的相关性，并且在监督的对比预训练期间仅推开其他完全不同的对话框。为了充分利用未标记的对话，还增加了基本的自我监督对比损失，以完善学习的表示。实验表明，我们的方法可以在DialogLue基准测试中实现新的最新结果，该基准由七个数据集和四个流行的对话框组成。为了获得可重复性，我们在https://github.com/alibabaresearch/damo-convai/tree/main/main/space-2上发布代码和数据。

本文旨在通过探索基于神经网络的方法（称为Sun）中的内在不确定性来提高文本到SQL解析的性能。从数据不确定性的角度来看，可以从多个语义等效的问题中学到单个SQL。从以前仅限于一对一映射的方法中不同，我们提出了一个数据不确定性限制来探索潜在的互补语义语义多个语义等效问题（多对一）中的信息，并以减少的虚假关联来学习稳健的特征表示。通过这种方式，我们可以降低学习表示的敏感性并改善解析器的鲁棒性。从模型的不确定性角度来看，神经网络的权重之间通常存在结构信息（依赖性）。为了提高神经文本到SQL解析器的普遍性和稳定性，我们提出了模型不确定性约束，以通过强制执行不同扰动编码网络的输出表示形式来完善查询表示形式，以使其彼此一致。在五个基准数据集上进行的广泛实验表明，我们的方法显着优于强大的竞争对手，并实现了新的最新结果。为了获得可重复性，我们在https://github.com/alibabaresearch/damo-convai/tree/main/main/sunsql上发布代码和数据。

近年来，多视图学习迅速发展。尽管许多先前的研究都认为每个实例都出现在所有视图中，但在现实世界应用程序中很常见，从某些视图中丢失实例，从而导致多视图数据不完整。为了解决这个问题，我们提出了一个新型潜在的异质图网络（LHGN），以实现不完整的多视图学习，该学习旨在以灵活的方式尽可能充分地使用多个不完整的视图。通过学习统一的潜在代表，隐含地实现了不同观点之间一致性和互补性之间的权衡。为了探索样本与潜在表示之间的复杂关系，首次提出了邻域约束和视图约束，以构建异质图。最后，为了避免训练和测试阶段之间的任何不一致之处，基于图形学习的分类任务应用了转导学习技术。对现实世界数据集的广泛实验结果证明了我们模型对现有最新方法的有效性。

文本到SQL解析是一项必不可少且具有挑战性的任务。文本到SQL解析的目的是根据关系数据库提供的证据将自然语言（NL）问题转换为其相应的结构性查询语言（SQL）。来自数据库社区的早期文本到SQL解析系统取得了显着的进展，重度人类工程和用户与系统的互动的成本。近年来，深层神经网络通过神经生成模型显着提出了这项任务，该模型会自动学习从输入NL问题到输出SQL查询的映射功能。随后，大型的预训练的语言模型将文本到SQL解析任务的最新作品带到了一个新级别。在这项调查中，我们对文本到SQL解析的深度学习方法进行了全面的评论。首先，我们介绍了文本到SQL解析语料库，可以归类为单转和多转。其次，我们提供了预先训练的语言模型和现有文本解析方法的系统概述。第三，我们向读者展示了文本到SQL解析所面临的挑战，并探索了该领域的一些潜在未来方向。

长期以来，可以将可以应用于新数据库的文本到SQL解析器的重要性已得到认可，实现此目标的关键步骤是架构链接，即在生成SQL时正确地识别未见列或表的提及。在这项工作中，我们提出了一个新颖的框架，以通过基于PoinCar \'e距离指标的探测程序从大规模预训练的语言模型（PLM）中引起关系结构，并使用诱导的关系来增强基于图的解析器为了更好的模式链接。与常用的基于规则的架构链接方法相比，我们发现探测关系也可以稳健地捕获语义对应关系，即使提及和实体的表面形式不同。此外，我们的探测过程完全不受监督，不需要其他参数。广泛的实验表明，我们的框架在三个基准测试中设定了新的最新性能。我们从经验上验证我们的探测程序确实可以通过定性分析找到所需的关系结构。

最近训练模型通过利用大规模文本语料库来改善神经网络的上下文表示能力，显着提高了各种NLP任务的性能。大型预培训语言模型也已应用于表语义解析的区域。然而，现有的预训练方法没有仔细探索问题与相应的数据库模式之间的明确互动关系，这是揭示其语义和结构对应的关键成分。此外，在架构接地背景下的问知表示学习在预训练目标中受到更少的关注。为了减轻这些问题，本文设计了两种新的预训练目标，将所需的归纳偏差将所需的归纳偏差施加到表前的学习表现-训练。我们进一步提出了一种模式感知课程学习方法来减轻噪声的影响，并以易于努力的方式从预训练数据中学习。我们通过在两个基准，蜘蛛和罢工中进行微调，评估我们预先接受训练的框架。结果表明，与各种基线相比，我们的预训练目标和课程的有效性。

Text-to-SQL旨在将自然语言问题映射到SQL查询。基于草图的方法与执行引导的（例如）解码策略相结合，在WikiSQL基准上显示了强烈性能。然而，执行引导的解码依赖于数据库执行，这显着降低了推理过程，因此对于许多真实世界的应用程序来说是不令人满意的。在本文中，我们介绍了模式依赖性指导多任务文本到SQL模型（SDSQL）来指导网络以有效地捕获问题和模式之间的交互。所提出的模型优先于两个设置中的所有现有方法，而且没有例如例如。我们展示了架构依赖学习部分涵盖了诸如益处，例如，减轻了对它的需求。没有例如在推理期间显着减少时间消耗的SDSQL，仅牺牲少量性能，并为下游应用提供更多的灵活性。

In this paper we explore the task of modeling (semi) structured object sequences; in particular we focus our attention on the problem of developing a structure-aware input representation for such sequences. In such sequences, we assume that each structured object is represented by a set of key-value pairs which encode the attributes of the structured object. Given a universe of keys, a sequence of structured objects can then be viewed as an evolution of the values for each key, over time. We encode and construct a sequential representation using the values for a particular key (Temporal Value Modeling - TVM) and then self-attend over the set of key-conditioned value sequences to a create a representation of the structured object sequence (Key Aggregation - KA). We pre-train and fine-tune the two components independently and present an innovative training schedule that interleaves the training of both modules with shared attention heads. We find that this iterative two part-training results in better performance than a unified network with hierarchical encoding as well as over, other methods that use a {\em record-view} representation of the sequence \cite{de2021transformers4rec} or a simple {\em flattened} representation of the sequence. We conduct experiments using real-world data to demonstrate the advantage of interleaving TVM-KA on multiple tasks and detailed ablation studies motivating our modeling choices. We find that our approach performs better than flattening sequence objects and also allows us to operate on significantly larger sequences than existing methods.

Deploying reliable deep learning techniques in interdisciplinary applications needs learned models to output accurate and ({even more importantly}) explainable predictions. Existing approaches typically explicate network outputs in a post-hoc fashion, under an implicit assumption that faithful explanations come from accurate predictions/classifications. We have an opposite claim that explanations boost (or even determine) classification. That is, end-to-end learning of explanation factors to augment discriminative representation extraction could be a more intuitive strategy to inversely assure fine-grained explainability, e.g., in those neuroimaging and neuroscience studies with high-dimensional data containing noisy, redundant, and task-irrelevant information. In this paper, we propose such an explainable geometric deep network dubbed as NeuroExplainer, with applications to uncover altered infant cortical development patterns associated with preterm birth. Given fundamental cortical attributes as network input, our NeuroExplainer adopts a hierarchical attention-decoding framework to learn fine-grained attentions and respective discriminative representations to accurately recognize preterm infants from term-born infants at term-equivalent age. NeuroExplainer learns the hierarchical attention-decoding modules under subject-level weak supervision coupled with targeted regularizers deduced from domain knowledge regarding brain development. These prior-guided constraints implicitly maximizes the explainability metrics (i.e., fidelity, sparsity, and stability) in network training, driving the learned network to output detailed explanations and accurate classifications. Experimental results on the public dHCP benchmark suggest that NeuroExplainer led to quantitatively reliable explanation results that are qualitatively consistent with representative neuroimaging studies.

Forecasts by the European Centre for Medium-Range Weather Forecasts (ECMWF; EC for short) can provide a basis for the establishment of maritime-disaster warning systems, but they contain some systematic biases.The fifth-generation EC atmospheric reanalysis (ERA5) data have high accuracy, but are delayed by about 5 days. To overcome this issue, a spatiotemporal deep-learning method could be used for nonlinear mapping between EC and ERA5 data, which would improve the quality of EC wind forecast data in real time. In this study, we developed the Multi-Task-Double Encoder Trajectory Gated Recurrent Unit (MT-DETrajGRU) model, which uses an improved double-encoder forecaster architecture to model the spatiotemporal sequence of the U and V components of the wind field; we designed a multi-task learning loss function to correct wind speed and wind direction simultaneously using only one model. The study area was the western North Pacific (WNP), and real-time rolling bias corrections were made for 10-day wind-field forecasts released by the EC between December 2020 and November 2021, divided into four seasons. Compared with the original EC forecasts, after correction using the MT-DETrajGRU model the wind speed and wind direction biases in the four seasons were reduced by 8-11% and 9-14%, respectively. In addition, the proposed method modelled the data uniformly under different weather conditions. The correction performance under normal and typhoon conditions was comparable, indicating that the data-driven mode constructed here is robust and generalizable.