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.

Text-to-SQL semantic parsing is an important NLP task, which greatly facilitates the interaction between users and the database and becomes the key component in many human-computer interaction systems. Much recent progress in text-to-SQL has been driven by large-scale datasets, but most of them are centered on English. In this work, we present MultiSpider, the largest multilingual text-to-SQL dataset which covers seven languages (English, German, French, Spanish, Japanese, Chinese, and Vietnamese). Upon MultiSpider, we further identify the lexical and structural challenges of text-to-SQL (caused by specific language properties and dialect sayings) and their intensity across different languages. Experimental results under three typical settings (zero-shot, monolingual and multilingual) reveal a 6.1% absolute drop in accuracy in non-English languages. Qualitative and quantitative analyses are conducted to understand the reason for the performance drop of each language. Besides the dataset, we also propose a simple schema augmentation framework SAVe (Schema-Augmentation-with-Verification), which significantly boosts the overall performance by about 1.8% and closes the 29.5% performance gap across languages.

Table-and-text hybrid question answering (HybridQA) is a widely used and challenging NLP task commonly applied in the financial and scientific domain. The early research focuses on migrating other QA task methods to HybridQA, while with further research, more and more HybridQA-specific methods have been present. With the rapid development of HybridQA, the systematic survey is still under-explored to summarize the main techniques and advance further research. So we present this work to summarize the current HybridQA benchmarks and methods, then analyze the challenges and future directions of this task. The contributions of this paper can be summarized in three folds: (1) first survey, to our best knowledge, including benchmarks, methods and challenges for HybridQA; (2) systematic investigation with the reasonable comparison of the existing systems to articulate their advantages and shortcomings; (3) detailed analysis of challenges in four important dimensions to shed light on future directions.

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.

Pre-trained Language Model (PLM) has become a representative foundation model in the natural language processing field. Most PLMs are trained with linguistic-agnostic pre-training tasks on the surface form of the text, such as the masked language model (MLM). To further empower the PLMs with richer linguistic features, in this paper, we aim to propose a simple but effective way to learn linguistic features for pre-trained language models. We propose LERT, a pre-trained language model that is trained on three types of linguistic features along with the original MLM pre-training task, using a linguistically-informed pre-training (LIP) strategy. We carried out extensive experiments on ten Chinese NLU tasks, and the experimental results show that LERT could bring significant improvements over various comparable baselines. Furthermore, we also conduct analytical experiments in various linguistic aspects, and the results prove that the design of LERT is valid and effective. Resources are available at https://github.com/ymcui/LERT

提示方法被认为是几次自然语言处理的关键进展之一。最近对基于离散令牌的``硬提示''转移到连续``软提示''的最新研究，这些提示将可学习的向量用作伪提示代币并实现更好的性能。尽管显示出有希望的前景，但观察到这些软宣传的方法在很大程度上依赖良好的初始化来生效。不幸的是，获得软提示的完美初始化需要了解内在语言模型的工作和精心设计，这绝非易事，必须从头开始重新启动每个新任务。为了解决此问题，我们提出了一种称为Metaprompting的广义软提示方法，该方法采用了良好认可的模型 - 静态元学习算法，以自动找到更好的及时初始化，从而快速适应新的促进任务。问题并在四个不同的数据集上带来了显着改善（1次设置的准确性提高了6分），从而实现了新的最新性能。

在本文中，我们介绍了CTC 2021的概述，这是针对母语人士的中文文本校正任务。我们详细描述了任务定义以及培训和评估的数据。我们还总结了该任务参与者调查的方法。我们希望为此任务收集和注释的数据集可以促进并加快该研究领域的未来发展。因此，伪培训数据，金标准验证数据和整个排行榜可在https://destwang.github.io/ctc2021-explorer/上在线公开获取。

Knowledge graph embedding (KGE) models learn the representation of entities and relations in knowledge graphs. Distance-based methods show promising performance on link prediction task, which predicts the result by the distance between two entity representations. However, most of these methods represent the head entity and tail entity separately, which limits the model capacity. We propose two novel distance-based methods named InterHT and InterHT+ that allow the head and tail entities to interact better and get better entity representation. Experimental results show that our proposed method achieves the best results on ogbl-wikikg2 dataset.

目前对语言理解（SLU）的研究重大仅限于简单的设置：基于纯文本的SLU，它将用户话语为输入并生成其相应的语义帧（例如，意图和插槽）。不幸的是，当话语是语义模糊的话语时，这种简单的设置可能无法在复杂的真实情景中工作，这不能通过基于文本的SLU模型来实现的。在本文中，我们首先介绍了一种新的和重要任务，基于个人资料的口语语言理解（ProSlu），这需要不仅依赖于纯文本的模型，而且需要支持的资料配置文件，以预测正确的意图和插槽。为此，我们进一步引入了一个具有超过5K的大规模的汉语数据集及其相应的支持简档信息（知识图（kg），用户配置文件（向上），上下文意识（CA））。此外，我们还评估了多个最先进的基线模型，并探索多级知识适配器，以有效地结合资料信息。实验结果表明，当话语是语义模糊的，我们所提出的框架可以有效地融合了句子级意图检测和令牌级槽填充的支持信息，所以所有现有的基于文本的SLU模型都无法工作。最后，我们总结了关键挑战，为未来方向提供了新的观点，希望促进研究。

数据增强是自然语言处理（NLP）模型的鲁棒性评估的重要组成部分，以及增强他们培训的数据的多样性。在本文中，我们呈现NL-Cogmenter，这是一种新的参与式Python的自然语言增强框架，它支持创建两个转换（对数据的修改）和过滤器（根据特定功能的数据拆分）。我们描述了框架和初始的117个变换和23个过滤器，用于各种自然语言任务。我们通过使用其几个转换来分析流行自然语言模型的鲁棒性来证明NL-Upmenter的功效。基础架构，Datacards和稳健性分析结果在NL-Augmenter存储库上公开可用（\ url {https://github.com/gem-benchmark/nl-augmenter}）。