在过去的几年中，临床笔记中的问题回答（QA）引起了很多关注。临床领域中现有的机器阅读理解方法只能处理有关单个临床文本的问题，并且无法检索有关多个患者及其临床笔记的信息。为了处理更复杂的问题，我们旨在从临床注释中创建知识库，以将不同的患者和临床笔记联系起来，并进行知识基础问题答案（KBQA）。根据N2C2数据集中可用的专家注释，我们首先创建了ClinicalKBQA数据集，其中包括大约9K QA对，并使用300多个问题模板涵盖了有关七个医学主题的问题。然后，我们研究了KBQA的一种基于注意力的方面推理（AAR）方法，并分析了答案的不同方面（例如，实体，类型，路径和上下文）对预测的影响。由于设计精良的编码器和注意力机制，AAR方法可实现更好的性能。从我们的实验中，我们发现这两个方面，类型和路径都使模型能够识别满足一般条件的答案，并产生较低的精度和更高的回忆。另一方面，各个方面，实体和上下文通过特定于节点的信息限制答案，并导致更高的精度和较低的回忆。

自动问题应答（QA）系统的目的是以时间有效的方式向用户查询提供答案。通常在数据库（或知识库）或通常被称为语料库的文件集合中找到答案。在过去的几十年里，收购知识的扩散，因此生物医学领域的新科学文章一直是指数增长。因此，即使对于领域专家，也难以跟踪域中的所有信息。随着商业搜索引擎的改进，用户可以在某些情况下键入其查询并获得最相关的一小组文档，以及在某些情况下从文档中的相关片段。但是，手动查找所需信息或答案可能仍然令人疑惑和耗时。这需要开发高效的QA系统，该系统旨在为用户提供精确和精确的答案提供了生物医学领域的自然语言问题。在本文中，我们介绍了用于开发普通域QA系统的基本方法，然后彻底调查生物医学QA系统的不同方面，包括使用结构化数据库和文本集合的基准数据集和几种提出的方​​法。我们还探讨了当前系统的局限性，并探索潜在的途径以获得进一步的进步。

知识基础问题回答（KBQA）旨在通过知识库（KB）回答问题。早期研究主要集中于回答有关KB的简单问题，并取得了巨大的成功。但是，他们在复杂问题上的表现远非令人满意。因此，近年来，研究人员提出了许多新颖的方法，研究了回答复杂问题的挑战。在这项调查中，我们回顾了KBQA的最新进展，重点是解决复杂问题，这些问题通常包含多个主题，表达复合关系或涉及数值操作。详细说明，我们从介绍复杂的KBQA任务和相关背景开始。然后，我们描述用于复杂KBQA任务的基准数据集，并介绍这些数据集的构建过程。接下来，我们提出两个复杂KBQA方法的主流类别，即基于语义解析的方法（基于SP）的方法和基于信息检索的方法（基于IR）。具体而言，我们通过流程设计说明了他们的程序，并讨论了它们的主要差异和相似性。之后，我们总结了这两类方法在回答复杂问题时会遇到的挑战，并解释了现有工作中使用的高级解决方案和技术。最后，我们结论并讨论了与复杂的KBQA有关的几个有希望的方向，以进行未来的研究。

知识图表问题基于信息检索旨在通过从大型知识图表中检索答案来回答问题来回答（即，kgqa）。大多数现有方法首先粗略地检索可能包含候选答案的知识子图（KSG），然后搜索子图中的确切答案。然而，粗略检索的KSG可以包含数千个候选节点，因为查询中涉及的知识图通常是大规模的。为了解决这个问题，我们首先建议通过新的子图分区算法将检索到的ksg分区为几个较小的子ksgs，然后呈现一个图形增强学习，以便测量模型以从中选择排名的子ksgs。我们所提出的模型结合了新的子图匹配网络，以捕获问题和子图中的全局交互以及增强的双边多视角匹配模型，以捕获局部交互。最后，我们分别在全KSG和排名级分ksg上应用答案选择模型，以验证我们提出的图形增强学习的效果。多个基准数据集的实验结果表明了我们方法的有效性。

自然语言处理（NLP）是一个人工智能领域，它应用信息技术来处理人类语言，在一定程度上理解并在各种应用中使用它。在过去的几年中，该领域已经迅速发展，现在采用了深层神经网络的现代变体来从大型文本语料库中提取相关模式。这项工作的主要目的是调查NLP在药理学领域的最新使用。正如我们的工作所表明的那样，NLP是药理学高度相关的信息提取和处理方法。它已被广泛使用，从智能搜索到成千上万的医疗文件到在社交媒体中找到对抗性药物相互作用的痕迹。我们将覆盖范围分为五个类别，以调查现代NLP方法论，常见的任务，相关的文本数据，知识库和有用的编程库。我们将这五个类别分为适当的子类别，描述其主要属性和想法，并以表格形式进行总结。最终的调查介绍了该领域的全面概述，对从业者和感兴趣的观察者有用。

Machine reading comprehension (MRC) is a long-standing topic in natural language processing (NLP). The MRC task aims to answer a question based on the given context. Recently studies focus on multi-hop MRC which is a more challenging extension of MRC, which to answer a question some disjoint pieces of information across the context are required. Due to the complexity and importance of multi-hop MRC, a large number of studies have been focused on this topic in recent years, therefore, it is necessary and worth reviewing the related literature. This study aims to investigate recent advances in the multi-hop MRC approaches based on 31 studies from 2018 to 2022. In this regard, first, the multi-hop MRC problem definition will be introduced, then 31 models will be reviewed in detail with a strong focus on their multi-hop aspects. They also will be categorized based on their main techniques. Finally, a fine-grain comprehensive comparison of the models and techniques will be presented.

医学视觉问题应答（VQA）是医疗人工智能和流行的VQA挑战的组合。鉴于医学形象和在自然语言中的临床相关问题，预计医疗VQA系统将预测符号和令人信服的答案。虽然一般域VQA已被广泛研究，但医疗VQA仍然需要特定的调查和探索，因为它的任务特征是。在本调查的第一部分，我们涵盖并讨论了关于数据源，数据数量和任务功能的公开可用的医疗VQA数据集。在第二部分中，我们审查了医疗VQA任务中使用的方法。在最后，我们分析了该领域的一些有效的挑战，并讨论了未来的研究方向。

Answering complex questions over textual resources remains a challenging problem$\unicode{x2013}$especially when interpreting the fine-grained relationships among multiple entities that occur within a natural-language question or clue. Curated knowledge bases (KBs), such as YAGO, DBpedia, Freebase and Wikidata, have been widely used in this context and gained great acceptance for question-answering (QA) applications in the past decade. While current KBs offer a concise representation of structured knowledge, they lack the variety of formulations and semantic nuances as well as the context of information provided by the natural-language sources. With BigText-QA, we aim to develop an integrated QA system which is able to answer questions based on a more redundant form of a knowledge graph (KG) that organizes both structured and unstructured (i.e., "hybrid") knowledge in a unified graphical representation. BigText-QA thereby is able to combine the best of both worlds$\unicode{x2013}$a canonical set of named entities, mapped to a structured background KB (such as YAGO or Wikidata), as well as an open set of textual clauses providing highly diversified relational paraphrases with rich context information.

过去十年互联网上可用的信息和信息量增加。该数字化导致自动应答系统需要从冗余和过渡知识源中提取富有成效的信息。这些系统旨在利用自然语言理解（NLU）从此巨型知识源到用户查询中最突出的答案，从而取决于问题答案（QA）字段。问题答案涉及但不限于用户问题映射的步骤，以获取相关查询，检索相关信息，从检索到的信息等找到最佳合适的答案等。当前对深度学习模型的当前改进估计所有这些任务的令人信服的性能改进。在本综述工作中，根据问题的类型，答案类型，证据答案来源和建模方法进行分析QA场的研究方向。此细节随后是自动问题生成，相似性检测和语言的低资源可用性等领域的开放挑战。最后，提出了对可用数据集和评估措施的调查。

Biomedical knowledge graphs (KG) are heterogenous networks consisting of biological entities as nodes and relations between them as edges. These entities and relations are extracted from millions of research papers and unified in a single resource. The goal of biomedical multi-hop question-answering over knowledge graph (KGQA) is to help biologist and scientist to get valuable insights by asking questions in natural language. Relevant answers can be found by first understanding the question and then querying the KG for right set of nodes and relationships to arrive at an answer. To model the question, language models such as RoBERTa and BioBERT are used to understand context from natural language question. One of the challenges in KGQA is missing links in the KG. Knowledge graph embeddings (KGE) help to overcome this problem by encoding nodes and edges in a dense and more efficient way. In this paper, we use a publicly available KG called Hetionet which is an integrative network of biomedical knowledge assembled from 29 different databases of genes, compounds, diseases, and more. We have enriched this KG dataset by creating a multi-hop biomedical question-answering dataset in natural language for testing the biomedical multi-hop question-answering system and this dataset will be made available to the research community. The major contribution of this research is an integrated system that combines language models with KG embeddings to give highly relevant answers to free-form questions asked by biologists in an intuitive interface. Biomedical multi-hop question-answering system is tested on this data and results are highly encouraging.

由于关键字相关互联网页面的返回，根据关键字检索的搜索引擎不再适应智能互联网时代的信息获取方式。如何快速，准确和有效地获取来自大规模互联网数据的用户所需的信息已成为迫切需要解决的关键问题之一。我们提出了一个基于结构化KB和非结构化数据的智能质疑答案系统，称为OpenQA，其中用户可以提供查询问题，并且模型可以快速向用户提供准确的答案。我们基于语义解析和深度表示学习的KBQA结构化问题回答，以及基于检索和神经机阅读理解的两级非结构化问题回答，并通过OpenQA中的变压器应答选择模块回归最高概率的最终答案。我们对我们构建的数据集进行了初步实验，实验结果证明了提出的智能问题应答系统的有效性。与此同时，OpenQA平台的每个模块的核心技术仍处于学术热点的最前沿，并基于这些学术热点进一步探索了OpenQA的理论本质和富集。

Multi-hop Machine reading comprehension is a challenging task with aim of answering a question based on disjoint pieces of information across the different passages. The evaluation metrics and datasets are a vital part of multi-hop MRC because it is not possible to train and evaluate models without them, also, the proposed challenges by datasets often are an important motivation for improving the existing models. Due to increasing attention to this field, it is necessary and worth reviewing them in detail. This study aims to present a comprehensive survey on recent advances in multi-hop MRC evaluation metrics and datasets. In this regard, first, the multi-hop MRC problem definition will be presented, then the evaluation metrics based on their multi-hop aspect will be investigated. Also, 15 multi-hop datasets have been reviewed in detail from 2017 to 2022, and a comprehensive analysis has been prepared at the end. Finally, open issues in this field have been discussed.

在商业航空域中，有大量文件，例如事故报告（NTSB，ASRS）和监管指令（ADS）。有必要有效地访问这些多样化的存储库，以便在航空业中的服务需求，例如维护，合规性和安全性。在本文中，我们提出了一个基于深度学习的知识图（kg）基于深度学习（DL）的问题答案（QA）航空安全系统。我们从飞机事故报告中构建了知识图，并向研究人员社区贡献了这一资源。该资源的功效由上述质量保证系统测试和证明。根据上述文档构建的自然语言查询将转换为SPARQL（RDF图数据库的接口语言）查询并回答。在DL方面，我们有两个不同的质量检查模型：（i）BERT QA，它是通道检索（基于句子的）和问题答案（基于BERT）的管道，以及（ii）最近发布的GPT-3。我们根据事故报告创建的一系列查询评估系统。我们组合的QA系统在GPT-3上的准确性增长了9.3％，比Bert QA增加了40.3％。因此，我们推断出KG-DL的性能比单一表现更好。

访问公共知识库中可用的大量信息可能对那些不熟悉的SPARQL查询语言的用户可能很复杂。SPARQL中自然语言提出的问题的自动翻译有可能克服这个问题。基于神经机翻译的现有系统非常有效，但在识别出识别出训练集的词汇（OOV）的单词中很容易失败。查询大型本体的时，这是一个严重的问题。在本文中，我们将命名实体链接，命名实体识别和神经计算机翻译相结合，以将自然语言问题的自动转换为SPARQL查询。我们凭经验证明，我们的方法比在纪念碑，QALD-9和LC-QUAD V1上运行实验，我们的方法比现有方法更有效，并且对OOV单词进行了更有效的，并且是现有的方法，这些方法是众所周知的DBPedia的相关数据集。

由于对高效有效的大数据分析解决方案的需求，医疗保健行业中数据分析的合并已取得了重大进展。知识图（KGS）已在该领域证明了效用，并且植根于许多医疗保健应用程序，以提供更好的数据表示和知识推断。但是，由于缺乏代表性的kg施工分类法，该指定领域中的几种现有方法不足和劣等。本文是第一个提供综合分类法和鸟类对医疗kg建筑的眼光的看法。此外，还对与各种医疗保健背景相关的学术工作中最新的技术进行了彻底的检查。这些技术是根据用于知识提取的方法，知识库和来源的类型以及合并评估协议的方法进行了严格评估的。最后，报道和讨论了文献中的一些研究发现和现有问题，为这个充满活力的地区开放了未来研究的视野。

使用从预先接受训练的语言模型（LMS）和知识图表（LMS）和知识图表（kgs）回答问题的问题提出了两个挑战：给定QA上下文（问答选择），方法需要（i）从大型千克识别相关知识，（ii）对QA上下文和kg进行联合推理。在这项工作中，我们提出了一种新的模型，QA-GNN，它通过两个关键创新解决了上述挑战：（i）相关评分，我们使用LMS来估计KG节点相对于给定的QA上下文的重要性，以及（ii）联合推理，我们将QA上下文和kg连接到联合图，并通过图形神经网络相互更新它们的表示。我们评估了QA基准的模型（CommanSeaseQA，OpenBookQA）和生物医学（MedQa-USMLE）域名。QA-GNN优于现有的LM和LM + kg模型，并表现出可解释和结构化推理的能力，例如，正确处理问题的否定。

知识图表（kg）作为从大型自然语言文本语料库中举行蒸馏信息的伟大工具。查询知识图表的自然语言问题对于这些信息的人类消费至关重要。通常通过将自然语言查询转换为结构化查询，然后在kg上触发结构化查询来解决此问题。在文献中的知识图中直接回答模型很少。查询转换模型和直接模型都需要与知识图表的域有关的特定培训数据。在这项工作中，我们将通过知识图表的自然语言问题转换为前提假设对的推理问题。使用培训的深度学习模型进行转换后的代理推理问题，我们为原始自然语言查询问题提供了解决方案。我们的方法在MetaQA数据集中实现了超过90％的准确性，击败现有的最先进。我们还提出了一种推论称为分层复发路径编码器（HRPE）的模型。可以微调推断模型以跨越跨越培训数据的域使用。我们的方法不需要大型域特定的培训数据来查询来自不同域的新知识图表。

Multi-hop Question Answering over Knowledge Graph~(KGQA) aims to find the answer entities that are multiple hops away from the topic entities mentioned in a natural language question on a large-scale Knowledge Graph (KG). To cope with the vast search space, existing work usually adopts a two-stage approach: it firstly retrieves a relatively small subgraph related to the question and then performs the reasoning on the subgraph to accurately find the answer entities. Although these two stages are highly related, previous work employs very different technical solutions for developing the retrieval and reasoning models, neglecting their relatedness in task essence. In this paper, we propose UniKGQA, a novel approach for multi-hop KGQA task, by unifying retrieval and reasoning in both model architecture and parameter learning. For model architecture, UniKGQA consists of a semantic matching module based on a pre-trained language model~(PLM) for question-relation semantic matching, and a matching information propagation module to propagate the matching information along the edges on KGs. For parameter learning, we design a shared pre-training task based on question-relation matching for both retrieval and reasoning models, and then propose retrieval- and reasoning-oriented fine-tuning strategies. Compared with previous studies, our approach is more unified, tightly relating the retrieval and reasoning stages. Extensive experiments on three benchmark datasets have demonstrated the effectiveness of our method on the multi-hop KGQA task. Our codes and data are publicly available at https://github.com/RUCAIBox/UniKGQA.

The meaningful use of electronic health records (EHR) continues to progress in the digital era with clinical decision support systems augmented by artificial intelligence. A priority in improving provider experience is to overcome information overload and reduce the cognitive burden so fewer medical errors and cognitive biases are introduced during patient care. One major type of medical error is diagnostic error due to systematic or predictable errors in judgment that rely on heuristics. The potential for clinical natural language processing (cNLP) to model diagnostic reasoning in humans with forward reasoning from data to diagnosis and potentially reduce the cognitive burden and medical error has not been investigated. Existing tasks to advance the science in cNLP have largely focused on information extraction and named entity recognition through classification tasks. We introduce a novel suite of tasks coined as Diagnostic Reasoning Benchmarks, DR.BENCH, as a new benchmark for developing and evaluating cNLP models with clinical diagnostic reasoning ability. The suite includes six tasks from ten publicly available datasets addressing clinical text understanding, medical knowledge reasoning, and diagnosis generation. DR.BENCH is the first clinical suite of tasks designed to be a natural language generation framework to evaluate pre-trained language models. Experiments with state-of-the-art pre-trained generative language models using large general domain models and models that were continually trained on a medical corpus demonstrate opportunities for improvement when evaluated in DR. BENCH. We share DR. BENCH as a publicly available GitLab repository with a systematic approach to load and evaluate models for the cNLP community.

Natural Language Generation (NLG) has improved exponentially in recent years thanks to the development of sequence-to-sequence deep learning technologies such as Transformer-based language models. This advancement has led to more fluent and coherent NLG, leading to improved development in downstream tasks such as abstractive summarization, dialogue generation and data-to-text generation. However, it is also apparent that deep learning based generation is prone to hallucinate unintended text, which degrades the system performance and fails to meet user expectations in many real-world scenarios. To address this issue, many studies have been presented in measuring and mitigating hallucinated texts, but these have never been reviewed in a comprehensive manner before. In this survey, we thus provide a broad overview of the research progress and challenges in the hallucination problem in NLG. The survey is organized into two parts: (1) a general overview of metrics, mitigation methods, and future directions; and (2) an overview of task-specific research progress on hallucinations in the following downstream tasks, namely abstractive summarization, dialogue generation, generative question answering, data-to-text generation, machine translation, and visual-language generation. This survey serves to facilitate collaborative efforts among researchers in tackling the challenge of hallucinated texts in NLG.