随着视频数量的越来越多,对技术的需求很大,可以帮助人们迅速导航到他们感兴趣的视频片段。但是,当前的视频理解主要理解主要是视频内容摘要,而几乎没有努力,而对探索视频的结构。受文本轮廓生成的启发,我们介绍了一项新颖的视频理解任务,即视频大纲生成(VOG)。该任务定义为包含两个子任务:(1)首先根据内容结构对视频进行分割,然后(2)为每个段生成一个标题。要学习和评估VOG,我们注释了一个10K+数据集,称为Duvog。具体来说,我们使用OCR工具来识别视频的字幕。然后,要求注释者将字幕分为章节,并将每个章节分为标题。在视频中,突出显示的文本往往是标题,因为它更有可能引起人们的注意。因此,我们提出了一个视觉字幕功能增强的视频大纲生成模型(VSENET),该模型将文本字幕及其视觉字体大小和位置作为输入。我们将VOG任务视为一个序列标记问题,该问题提取了跨标题的位置,然后将其重写以形成最终大纲。此外,基于视频概述和文本概述之间的相似性,我们使用大量文章带有章节标题来预先我们的模型。 Duvog上的实验表明,我们的模型在很大程度上胜过其他基线方法,对于视频分割水平达到了77.1的F1得分,对于标题生成级别的Rouge-L_F0.5的85.0。
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The lack of label data is one of the significant bottlenecks for Chinese Spelling Check (CSC). Existing researches use the method of automatic generation by exploiting unlabeled data to expand the supervised corpus. However, there is a big gap between the real input scenario and automatic generated corpus. Thus, we develop a competitive general speller ECSpell which adopts the Error Consistent masking strategy to create data for pretraining. This error consistency masking strategy is used to specify the error types of automatically generated sentences which is consistent with real scene. The experimental result indicates our model outperforms previous state-of-the-art models on the general benchmark. Moreover, spellers often work within a particular domain in real life. Due to lots of uncommon domain terms, experiments on our built domain specific datasets show that general models perform terribly. Inspired by the common practice of input methods, we propose to add an alterable user dictionary to handle the zero-shot domain adaption problem. Specifically, we attach a User Dictionary guided inference module (UD) to a general token classification based speller. Our experiments demonstrate that ECSpell$^{UD}$, namely ECSpell combined with UD, surpasses all the other baselines largely, even approaching the performance on the general benchmark.
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当今,分会一代成为在线视频的实用技术。本章断点使用户能够快速找到所需的零件并获得总结注释。但是,没有公共方法和数据集用于此任务。为了促进该方向的研究,我们介绍了一个名为Chapter-gen的新数据集,该数据集由大约10K用户生成的视频和带注释的章节信息组成。我们的数据收集过程是快速,可扩展的,不需要任何其他手动注释。在此数据集之外,我们设计了一个有效的基线,专门针对视频章节生成任务。捕获视频的两个方面,包括视觉动态和叙述文本。它分别将本地和全球视频功能分别用于本地化和标题生成。为了有效地解析长时间的视频,Skip滑动窗口机构旨在定位潜在的章节。并且开发了交叉注意的多模式融合模块,以汇总标题生成的本地功能。我们的实验表明,所提出的框架比现有方法取得了优越的结果,这表明即使在微调后也无法直接传输类似任务的方法设计。代码和数据集可在https://github.com/czt117/mvcg上找到。
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密集的视频字幕旨在确定输入视频中感兴趣的事件,并为每个事件生成描述性标题。先前的方法通常遵循两个阶段的生成过程,该过程首先提出了每个事件的段,然后为每个已确定的细分市场提供标题。大规模序列产生预处理的最新进展在统一各种任务的任务制定方面取得了巨大的成功,但是到目前为止,更复杂的任务(例如密集的视频字幕)无法完全利用这种强大的范式。在这项工作中,我们展示了如何将密集视频字幕的两个子任务与一个序列生成任务建模,并同时预测事件和相应的描述。在YouCook2和Vitt上进行的实验表现出令人鼓舞的结果,并表明训练复杂任务的可行性,例如集成到大规模预处理模型中的端到端密集的视频字幕。
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最近,由于其广泛的商业价值,从视觉丰富的文档(例如门票和简历)中自动提取信息已成为一个热门而重要的研究主题。大多数现有方法将此任务分为两个小节:用于从原始文档图像中获取纯文本的文本阅读部分以及用于提取密钥内容的信息提取部分。这些方法主要集中于改进第二个方法,同时忽略了这两个部分高度相关。本文提出了一个统一的端到端信息提取框架,从视觉上富含文档中提出,文本阅读和信息提取可以通过精心设计的多模式上下文块相互加强。具体而言,文本阅读部分提供了多模式功能,例如视觉,文本和布局功能。开发了多模式上下文块,以融合生成的多模式特征,甚至是从预训练的语言模型中获得的先验知识,以提供更好的语义表示。信息提取部分负责使用融合上下文功能生成密钥内容。该框架可以以端到端的可训练方式进行培训,从而实现全球优化。更重要的是,我们将视觉丰富的文档定义为跨两个维度的四个类别,即布局和文本类型。对于每个文档类别,我们提供或推荐相应的基准,实验设置和强大的基准,以弥补该研究领域缺乏统一评估标准的问题。报告了对四种基准测试的广泛实验(从固定布局到可变布局,从完整的文本到半未结构化的文本),证明了所提出的方法的有效性。数据,源代码和模型可用。
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学术研究是解决以前从未解决过的问题的探索活动。通过这种性质,每个学术研究工作都需要进行文献审查,以区分其Novelties尚未通过事先作品解决。在自然语言处理中,该文献综述通常在“相关工作”部分下进行。鉴于研究文件的其余部分和引用的论文列表,自动相关工作生成的任务旨在自动生成“相关工作”部分。虽然这项任务是在10年前提出的,但直到最近,它被认为是作为科学多文件摘要问题的变种。然而,即使在今天,尚未标准化了自动相关工作和引用文本生成的问题。在这项调查中,我们进行了一个元研究,从问题制定,数据集收集,方法方法,绩效评估和未来前景的角度来比较相关工作的现有文献,以便为读者洞察到国家的进步 - 最内容的研究,以及如何进行未来的研究。我们还调查了我们建议未来工作要考虑整合的相关研究领域。
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在视觉上丰富的文件(VRD)上的结构化文本理解是文档智能的重要组成部分。由于VRD中的内容和布局的复杂性,结构化文本理解是一项有挑战性的任务。大多数现有的研究将此问题与两个子任务结尾:实体标记和实体链接,这需要整体地了解令牌和段级别的文档的上下文。但是,很少的工作已经关注有效地从不同层次提取结构化数据的解决方案。本文提出了一个名为structext的统一框架,它对于处理两个子任务是灵活的,有效的。具体地,基于变压器,我们引入了一个段令牌对齐的编码器,以处理不同粒度水平的实体标记和实体链接任务。此外,我们设计了一种具有三个自我监督任务的新型预训练策略,以学习更丰富的代表性。 Structext使用现有屏蔽的视觉语言建模任务和新句子长度预测和配对框方向任务,以跨文本,图像和布局结合多模态信息。我们评估我们在分段级别和令牌级别的结构化文本理解的方法,并表明它优于最先进的同行,在Funsd,Srie和Ephoie数据集中具有显着优越的性能。
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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.
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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.
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跨度提取,旨在从纯文本中提取文本跨度(如单词或短语),是信息提取中的基本过程。最近的作品介绍了通过将跨度提取任务正式化为问题(QA正式化)的跨度提取任务来提高文本表示,以实现最先进的表现。然而,QA正规化并没有充分利用标签知识并遭受培训/推理的低效率。为了解决这些问题,我们介绍了一种新的范例来整合标签知识,并进一步提出一个小说模型,明确有效地将标签知识集成到文本表示中。具体而言,它独立地编码文本和标签注释,然后将标签知识集成到文本表示中,并使用精心设计的语义融合模块进行文本表示。我们在三个典型的跨度提取任务中进行广泛的实验:扁平的网,嵌套网和事件检测。实证结果表明,我们的方法在四个基准测试中实现了最先进的性能,而且分别将培训时间和推理时间降低76%和77%,与QA形式化范例相比。我们的代码和数据可在https://github.com/apkepers/lear中获得。
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The potential for agents, whether embodied or software, to learn by observing other agents performing procedures involving objects and actions is rich. Current research on automatic procedure learning heavily relies on action labels or video subtitles, even during the evaluation phase, which makes them infeasible in real-world scenarios. This leads to our question: can the human-consensus structure of a procedure be learned from a large set of long, unconstrained videos (e.g., instructional videos from YouTube) with only visual evidence? To answer this question, we introduce the problem of procedure segmentation-to segment a video procedure into category-independent procedure segments. Given that no large-scale dataset is available for this problem, we collect a large-scale procedure segmentation dataset with procedure segments temporally localized and described; we use cooking videos and name the dataset YouCook2. We propose a segment-level recurrent network for generating procedure segments by modeling the dependencies across segments. The generated segments can be used as pre-processing for other tasks, such as dense video captioning and event parsing. We show in our experiments that the proposed model outperforms competitive baselines in procedure segmentation.
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在科学研究中,该方法是解决科学问题和关键研究对象的必不可少手段。随着科学的发展,正在提出,修改和使用许多科学方法。作者在抽象和身体文本中描述了该方法的详细信息,并且反映该方法名称的学术文献中的关键实体称为方法实体。在大量的学术文献中探索各种方法实体有助于学者了解现有方法,为研究任务选择适当的方法并提出新方法。此外,方法实体的演变可以揭示纪律的发展并促进知识发现。因此,本文对方法论和经验作品进行了系统的综述,重点是从全文学术文献中提取方法实体,并努力使用这些提取的方法实体来建立知识服务。首先提出了本综述涉及的关键概念的定义。基于这些定义,我们系统地审查了提取和评估方法实体的方法和指标,重点是每种方法的利弊。我们还调查了如何使用提取的方法实体来构建新应用程序。最后,讨论了现有作品的限制以及潜在的下一步。
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连接视觉和语言在生成智能中起着重要作用。因此,已经致力于图像标题的大型研究工作,即用句法和语义有意义的句子描述图像。从2015年开始,该任务通常通过由Visual Encoder组成的管道和文本生成的语言模型来解决任务。在这些年来,两种组件通过对象区域,属性,介绍多模态连接,完全关注方法和伯特早期融合策略的利用而显着发展。但是,无论令人印象深刻的结果,图像标题的研究还没有达到结论性答案。这项工作旨在提供图像标题方法的全面概述,从视觉编码和文本生成到培训策略,数据集和评估度量。在这方面,我们量化地比较了许多相关的最先进的方法来确定架构和培训策略中最有影响力的技术创新。此外,讨论了问题的许多变体及其开放挑战。这项工作的最终目标是作为理解现有文献的工具,并突出显示计算机视觉和自然语言处理的研究领域的未来方向可以找到最佳的协同作用。
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Annotation of multimedia data by humans is time-consuming and costly, while reliable automatic generation of semantic metadata is a major challenge. We propose a framework to extract semantic metadata from automatically generated video captions. As metadata, we consider entities, the entities' properties, relations between entities, and the video category. We employ two state-of-the-art dense video captioning models with masked transformer (MT) and parallel decoding (PVDC) to generate captions for videos of the ActivityNet Captions dataset. Our experiments show that it is possible to extract entities, their properties, relations between entities, and the video category from the generated captions. We observe that the quality of the extracted information is mainly influenced by the quality of the event localization in the video as well as the performance of the event caption generation.
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本文对过去二十年来对自然语言生成(NLG)的研究提供了全面的审查,特别是与数据到文本生成和文本到文本生成深度学习方法有关,以及NLG的新应用技术。该调查旨在(a)给出关于NLG核心任务的最新综合,以及该领域采用的建筑;(b)详细介绍各种NLG任务和数据集,并提请注意NLG评估中的挑战,专注于不同的评估方法及其关系;(c)强调一些未来的强调和相对近期的研究问题,因为NLG和其他人工智能领域的协同作用而增加,例如计算机视觉,文本和计算创造力。
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In recent years, interest has arisen in using machine learning to improve the efficiency of automatic medical consultation and enhance patient experience. In this article, we propose two frameworks to support automatic medical consultation, namely doctor-patient dialogue understanding and task-oriented interaction. We create a new large medical dialogue dataset with multi-level finegrained annotations and establish five independent tasks, including named entity recognition, dialogue act classification, symptom label inference, medical report generation and diagnosis-oriented dialogue policy. We report a set of benchmark results for each task, which shows the usability of the dataset and sets a baseline for future studies. Both code and data is available from https://github.com/lemuria-wchen/imcs21.
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Future work sentences (FWS) are the particular sentences in academic papers that contain the author's description of their proposed follow-up research direction. This paper presents methods to automatically extract FWS from academic papers and classify them according to the different future directions embodied in the paper's content. FWS recognition methods will enable subsequent researchers to locate future work sentences more accurately and quickly and reduce the time and cost of acquiring the corpus. The current work on automatic identification of future work sentences is relatively small, and the existing research cannot accurately identify FWS from academic papers, and thus cannot conduct data mining on a large scale. Furthermore, there are many aspects to the content of future work, and the subdivision of the content is conducive to the analysis of specific development directions. In this paper, Nature Language Processing (NLP) is used as a case study, and FWS are extracted from academic papers and classified into different types. We manually build an annotated corpus with six different types of FWS. Then, automatic recognition and classification of FWS are implemented using machine learning models, and the performance of these models is compared based on the evaluation metrics. The results show that the Bernoulli Bayesian model has the best performance in the automatic recognition task, with the Macro F1 reaching 90.73%, and the SCIBERT model has the best performance in the automatic classification task, with the weighted average F1 reaching 72.63%. Finally, we extract keywords from FWS and gain a deep understanding of the key content described in FWS, and we also demonstrate that content determination in FWS will be reflected in the subsequent research work by measuring the similarity between future work sentences and the abstracts.
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确保适当的标点符号和字母外壳是朝向应用复杂的自然语言处理算法的关键预处理步骤。这对于缺少标点符号和壳体的文本源,例如自动语音识别系统的原始输出。此外,简短的短信和微博的平台提供不可靠且经常错误的标点符号和套管。本调查概述了历史和最先进的技术,用于恢复标点符号和纠正单词套管。此外,突出了当前的挑战和研究方向。
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谈话问题应答需要能够正确解释问题。然而,由于在日常谈话中难以理解共同参考和省略号的难度,目前的模型仍然不令人满意。尽管生成方法取得了显着的进展,但它们仍然被语义不完整陷入困境。本文提出了一种基于动作的方法来恢复问题的完整表达。具体地,我们首先在将相应的动作分配给每个候选跨度的同时定位问题中的共同引用或省略号的位置。然后,我们寻找与对话环境中的候选线索相关的匹配短语。最后,根据预测的操作,我们决定是否用匹配的信息替换共同参考或补充省略号。我们展示了我们对英语和中文发言权重写任务的方法的有效性,在RESTORATION-200K数据集中分别在3.9 \%和Rouge-L中提高了最先进的EM(完全匹配)。
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密集的视频字幕(DVC)旨在生成多句子描述,以阐明视频中的多个事件,这是具有挑战性,需要的视觉一致性,疑惑一致性和语言多样性。现有方法主要生成各个视频段的标题,缺乏适应全局视觉上下文和快速发展的视觉内容和文本描述之间的渐进对齐,这导致冗余和拼接描述。在本文中,我们介绍了信息流的概念,以模拟跨视频序列和标题的渐进信息。通过设计跨模型信息流对准机制,捕获和对齐的视觉和文本信息流,其在事件/主题演化上以更丰富的上下文和动态赋予标题处理。基于跨模型信息流对准模块,我们进一步提出了DVCFlow框架,它由全球本地视觉编码器组成,用于捕获每个视频段的全局功能和本地特征,以及用于产生标题的预先培训的标题生成器。对流行的ActivityNet标题和Youcookii数据集的广泛实验表明,我们的方法显着优于竞争基础,并根据主题和客观测试产生更多人类文本。
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