Code completion is a valuable topic in both academia and industry. Recently, large-scale mono-programming-lingual (MonoPL) pre-training models have been proposed to boost the performance of code completion. However, the code completion on low-resource programming languages (PL) is difficult for the data-driven paradigm, while there are plenty of developers using low-resource PLs. On the other hand, there are few studies exploring the effects of multi-programming-lingual (MultiPL) pre-training for the code completion, especially the impact on low-resource programming languages. To this end, we propose the MultiCoder to enhance the low-resource code completion via MultiPL pre-training and MultiPL Mixture-of-Experts (MoE) layers. We further propose a novel PL-level MoE routing strategy (PL-MoE) for improving the code completion on all PLs. Experimental results on CodeXGLUE and MultiCC demonstrate that 1) the proposed MultiCoder significantly outperforms the MonoPL baselines on low-resource programming languages, and 2) the PL-MoE module further boosts the performance on six programming languages. In addition, we analyze the effects of the proposed method in details and explore the effectiveness of our method in a variety of scenarios.

Natural language processing for programming, which aims to use NLP techniques to assist programming, has experienced an explosion in recent years. However, there is no literature that systematically reviews related work from the full spectrum. In this paper, we comprehensively investigate existing work, ranging from early deductive models to the latest competition-level models. Another advantage of this paper is the completeness of the technique category, which provides easy access to locating and comparing future works.

Aspect-based sentiment analysis (ABSA) aims at extracting opinionated aspect terms in review texts and determining their sentiment polarities, which is widely studied in both academia and industry. As a fine-grained classification task, the annotation cost is extremely high. Domain adaptation is a popular solution to alleviate the data deficiency issue in new domains by transferring common knowledge across domains. Most cross-domain ABSA studies are based on structure correspondence learning (SCL), and use pivot features to construct auxiliary tasks for narrowing down the gap between domains. However, their pivot-based auxiliary tasks can only transfer knowledge of aspect terms but not sentiment, limiting the performance of existing models. In this work, we propose a novel Syntax-guided Domain Adaptation Model, named SDAM, for more effective cross-domain ABSA. SDAM exploits syntactic structure similarities for building pseudo training instances, during which aspect terms of target domain are explicitly related to sentiment polarities. Besides, we propose a syntax-based BERT mask language model for further capturing domain-invariant features. Finally, to alleviate the sentiment inconsistency issue in multi-gram aspect terms, we introduce a span-based joint aspect term and sentiment analysis module into the cross-domain End2End ABSA. Experiments on five benchmark datasets show that our model consistently outperforms the state-of-the-art baselines with respect to Micro-F1 metric for the cross-domain End2End ABSA task.

预训练模型已在许多代码智能任务中有效。这些模型在大规模未标记的语料库中进行了预训练，然后在下游任务中进行了微调。但是，由于预训练和下游任务的输入是不同的形式，因此很难充分探索预训练模型的知识。此外，微调的性能强烈依赖于下游数据的量，而实际上，具有稀缺数据的场景很常见。自然语言处理（NLP）领域的最新研究表明，迅速调整，一种调整的新范式，减轻上述问题并在各种NLP任务中实现了有希望的结果。在迅速调整中，在调整过程中插入的提示提供了特定于任务的知识，这对于具有相对较少数据的任务特别有益。在本文中，我们凭经验评估了代码智能任务中迅速调整的用法和效果。我们对流行的预训练模型Codebert和codet5进行及时调整，并尝试三个代码智能任务，包括缺陷预测，代码摘要和代码翻译。我们的实验结果表明，在所有三个任务中，迅速调整始终优于微调。此外，及时调整在低资源场景中显示出很大的潜力，例如，对于代码摘要，平均将微调的BLEU分数提高了26％以上。我们的结果表明，我们可以调整代码智能任务的迅速调整，以实现更好的性能，尤其是在缺乏特定于任务的数据时，我们可以调整及时调整。

现实世界数据通常存在长尾分布。对不平衡数据的培训倾向于呈现神经网络在头部上表现良好，而尾部课程则更加差。尾班的培训实例的严重稀疏性是主要挑战，这导致培训期间的偏见分配估计。丰富的努力已经致力于改善挑战，包括数据重新采样和综合尾班的新培训实例。然而，没有先前的研究已经利用了从头课程转移到尾班的可转让知识，以校准尾舱的分布。在本文中，我们假设可以通过类似的头部级别来丰富尾部类，并提出一种名为标签感知分布校准Ladc的新型分布校准方法。 Ladc从相关的头部课程转移统计数据以推断尾部课程的分布。从校准分布的采样进一步促进重新平衡分类器。图像和文本的实验和文本长尾数据集表明，LADC显着优于现有方法。可视化还显示LADC提供更准确的分布估计。

代码摘要可帮助开发人员理解程序并减少在软件维护过程中推断程序功能的时间。最近的努力诉诸深度学习技术，例如序列到序列模型，以生成准确的代码摘要，其中基于变压器的方法已实现了有希望的性能。但是，在此任务域中，有效地将代码结构信息集成到变压器中的情况不足。在本文中，我们提出了一种名为SG-Trans的新方法，将代码结构属性纳入变压器。具体而言，我们将局部符号信息（例如，代码令牌和语句）和全局句法结构（例如，数据流程图）注入变压器的自我发项模块中。为了进一步捕获代码的层次结构特征，局部信息和全局结构旨在分布在下层和变压器高层的注意力头中。广泛的评估表明，SG-trans的表现优于最先进的方法。与表现最佳的基线相比，SG-Trans在流星评分方面仍然可以提高1.4％和2.0％，这是一个广泛用于测量发电质量的度量，分别在两个基准数据集上。

In this paper, we study the problem of knowledge-intensive text-to-SQL, in which domain knowledge is necessary to parse expert questions into SQL queries over domain-specific tables. We formalize this scenario by building a new Chinese benchmark KnowSQL consisting of domain-specific questions covering various domains. We then address this problem by presenting formulaic knowledge, rather than by annotating additional data examples. More concretely, we construct a formulaic knowledge bank as a domain knowledge base and propose a framework (ReGrouP) to leverage this formulaic knowledge during parsing. Experiments using ReGrouP demonstrate a significant 28.2% improvement overall on KnowSQL.

Weakly-supervised object localization aims to indicate the category as well as the scope of an object in an image given only the image-level labels. Most of the existing works are based on Class Activation Mapping (CAM) and endeavor to enlarge the discriminative area inside the activation map to perceive the whole object, yet ignore the co-occurrence confounder of the object and context (e.g., fish and water), which makes the model inspection hard to distinguish object boundaries. Besides, the use of CAM also brings a dilemma problem that the classification and localization always suffer from a performance gap and can not reach their highest accuracy simultaneously. In this paper, we propose a casual knowledge distillation method, dubbed KD-CI-CAM, to address these two under-explored issues in one go. More specifically, we tackle the co-occurrence context confounder problem via causal intervention (CI), which explores the causalities among image features, contexts, and categories to eliminate the biased object-context entanglement in the class activation maps. Based on the de-biased object feature, we additionally propose a multi-teacher causal distillation framework to balance the absorption of classification knowledge and localization knowledge during model training. Extensive experiments on several benchmarks demonstrate the effectiveness of KD-CI-CAM in learning clear object boundaries from confounding contexts and addressing the dilemma problem between classification and localization performance.

Dynamic treatment regimes assign personalized treatments to patients sequentially over time based on their baseline information and time-varying covariates. In mobile health applications, these covariates are typically collected at different frequencies over a long time horizon. In this paper, we propose a deep spectral Q-learning algorithm, which integrates principal component analysis (PCA) with deep Q-learning to handle the mixed frequency data. In theory, we prove that the mean return under the estimated optimal policy converges to that under the optimal one and establish its rate of convergence. The usefulness of our proposal is further illustrated via simulations and an application to a diabetes dataset.

Nowadays, time-stamped web documents related to a general news query floods spread throughout the Internet, and timeline summarization targets concisely summarizing the evolution trajectory of events along the timeline. Unlike traditional document summarization, timeline summarization needs to model the time series information of the input events and summarize important events in chronological order. To tackle this challenge, in this paper, we propose a Unified Timeline Summarizer (UTS) that can generate abstractive and extractive timeline summaries in time order. Concretely, in the encoder part, we propose a graph-based event encoder that relates multiple events according to their content dependency and learns a global representation of each event. In the decoder part, to ensure the chronological order of the abstractive summary, we propose to extract the feature of event-level attention in its generation process with sequential information remained and use it to simulate the evolutionary attention of the ground truth summary. The event-level attention can also be used to assist in extracting summary, where the extracted summary also comes in time sequence. We augment the previous Chinese large-scale timeline summarization dataset and collect a new English timeline dataset. Extensive experiments conducted on these datasets and on the out-of-domain Timeline 17 dataset show that UTS achieves state-of-the-art performance in terms of both automatic and human evaluations.