鉴于自然语言陈述,如何验证其对维基百科这样的大型文本知识来源的准确性吗?大多数现有的神经模型在不提供关于哪一部分错误的情况下出现问题的情况下会进行预测。在本文中,我们提出了Loren,一种可解释的事实验证的方法。我们分解了在短语级别的整个索赔的验证,其中短语的真实性用作解释,可以根据逻辑规则汇总到最终判决中。 Loren的关键洞察力是将索赔词如三值潜变量代表如下,由聚合逻辑规则规范化。最终索赔验证基于所有潜在的变量。因此,Loren享有可解释性的额外好处 - 很容易解释它如何通过索赔词来达到某些结果。关于公共事实验证基准的实验表明,Loren对以前的方法具有竞争力,同时享有忠实和准确的可解释性的优点。 Loren的资源可用于:https://github.com/jiangjiechen/loren。
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我们研究了检查问题的事实,旨在识别给定索赔的真实性。具体而言,我们专注于事实提取和验证(发烧)及其伴随数据集的任务。该任务包括从维基百科检索相关文件(和句子)并验证文件中的信息是否支持或驳斥所索赔的索赔。此任务至关重要,可以是假新闻检测和医疗索赔验证等应用程序块。在本文中,我们以通过以结构化和全面的方式呈现文献来更好地了解任务的挑战。我们通过分析不同方法的技术视角并讨论发热数据集的性能结果,描述了所提出的方法,这是最熟悉的和正式结构化的数据集,就是事实提取和验证任务。我们还迄今为止迄今为止确定句子检索组件的有益损失函数的最大实验研究。我们的分析表明,采样负句对于提高性能并降低计算复杂性很重要。最后,我们描述了开放的问题和未来的挑战,我们激励了未来的任务研究。
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Given a possibly false claim sentence, how can we automatically correct it with minimal editing? Existing methods either require a large number of pairs of false and corrected claims for supervised training or do not handle well errors spanning over multiple tokens within an utterance. In this paper, we propose VENCE, a novel method for factual error correction (FEC) with minimal edits. VENCE formulates the FEC problem as iterative sampling editing actions with respect to a target density function. We carefully design the target function with predicted truthfulness scores from an offline trained fact verification model. VENCE samples the most probable editing positions based on back-calculated gradients of the truthfulness score concerning input tokens and the editing actions using a distantly-supervised language model (T5). Experiments on a public dataset show that VENCE improves the well-adopted SARI metric by 5.3 (or a relative improvement of 11.8%) over the previous best distantly-supervised methods.
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事实验证系统通常依靠神经网络分类器来实现缺乏解释性的准确性预测。本文提出了验证器,该证明器使用SEQ2SEQ模型来生成基于自然逻辑的推论作为证明。这些证明包括声明中的跨度和检索的证据之间的词汇突变,每个突变都标有自然逻辑操作员。声称准确性仅基于这些操作员的序列确定。因此,这些证明是忠实的解释,这使验证者忠实于构造。目前,Proofver具有最高的标签精度,并且是发烧排行榜中第二高的得分。此外,它在具有反事实实例的数据集上的下一个最佳模型中提高了13.21%的积分,证明了其稳健性。作为解释,与基于注意的重点相比,证明与人类理由的重叠更好,并且证明可以帮助人类正确预测模型决策,而不是直接使用证据。
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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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现在,错误和虚假信息已成为我们安全和安全的全球威胁。为了应对在线错误信息的规模,一个可行的解决方案是通过检索和验证相关证据来自动对索赔进行事实检查。尽管在推动自动事实验证方面取得了最新进展,但仍缺乏对可能针对此类系统的攻击向量的全面评估。特别是,自动化事实验证过程可能容易受到其试图打击的确切虚假信息。在这项工作中,我们假设一个对手可以自动使用在线证据擦洗,以通过伪装相关证据或种植误导性的证据来破坏事实检查模型。我们首先提出了探索性分类法,该分类法涵盖了这两个目标和不同的威胁模型维度。在此指导下,我们设计并提出了几种潜在的攻击方法。我们表明,除了产生多样化和索赔一致的证据之外,还可以在证据中巧妙地修改索赔空位段。结果,我们在分类法的许多不同排列中高度降低了事实检查的表现。这些攻击也对索赔后的事后修改也很强大。我们的分析进一步暗示了在面对矛盾的证据时,模型推断的潜在局限性。我们强调,这些攻击可能会对此类模型的可检查和人类使用情况产生有害的影响,我们通过讨论未来防御的挑战和方向来得出结论。
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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。
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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.
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基于知识的视觉问题的问题涉及除了图像内容之外还涉及需要外部知识的问题。这些知识通常有各种形式,包括视觉,文本和致辞知识。使用更多知识来源,增加了检索更无关紧要或嘈杂的事实的可能性,使其充实并找到答案的挑战。为了解决这一挑战,我们使用外部知识(MAVEX)提出了多模态答案验证,其中该想法是根据答案特定知识检索验证一组有希望的答案候选者。而不是在大多数现有方法中搜索大量不相关的事实中的答案,Mavex旨在学习如何从嘈杂来源中提取相关知识,这是对每个答复候选者的信任,以及如何使用候选者那个来源。除了以维基百科句子和概念概念的形式之外,我们的多模态设置是第一个利用外部视觉知识(使用谷歌搜索的图像)。我们的实验与OK-VQA是一个具有挑战性的知识VQA数据集,证明了MAVEX实现了新的最先进的结果。我们的代码可在https://github.com/jialinwu17/mavex提供
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我们介绍了用于科学索赔核查的龙头克切者系统。鉴于科学索赔和含证据的研究摘要,Longchecker预测了一种可靠的标签,并根据索赔和摘要的共享编码,以多任务方式识别支持的基本原理。我们在SCIFact DataSet上执行实验,并发现Longchecker实现了最先进的性能。我们进行分析以了解这种改进的来源,并发现识别声明与报告科学发现之间的关系往往需要了解出现理由的背景。通过根据所有可用上下文进行标记决策,Longchecker在需要这种类型理解的情况下实现更好的性能。此外,我们表明LongChecker能够利用弱域内数据来利用弱势域数据,以方便为科学索赔核查的少量域适应。
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我们提出了索赔动物:事实检验和事实分析的新型潜在变量模型,该模型给出了索赔和一组检索的证据,可以共同学习:(i)该主张的相关证明是什么(ii)这一说法的真实性。我们建议以可解释的方式删除提供的全部相关性概率及其对最终准确性概率的贡献 - 最终的准确性概率与单位相关性概率的线性集合成正比。这样,可以清楚地识别出哪些来源的相关性在何种程度上朝着最终概率方面的范围。我们表明,我们的系统在发烧数据集上实现了最先进的结果,可与通常在传统事实检查管道中使用的两阶段系统相当,而通常使用的参数和计算较少。我们的分析表明,提出的方法进一步允许不仅了解哪些证明是相关的,而且还可以在没有直接监督的情况下获得支持和拒绝索赔的哪些证明。这不仅增加了解释性,而且还允许自动检测出与证据相互冲突的索赔。此外,我们研究模型在使用粗粒监督时是否可以学习细粒度的相关性线索。我们表明,我们的模型只能使用段落级相关性监督,可以实现竞争性的句子回顾。最后,朝着最优质的相关性跨度,我们表明我们的框架能够在令牌级别上识别相关性。为此,我们提出了一个专注于令牌级别的解释性的新基准 - 人类在相关证明中注释令牌,他们在做出判断时认为必不可少。然后,我们衡量这些注释与代币的相似之处是我们的模型的重点。我们的代码和数据集将在线发布。
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预训练的语言模型(PTLM)已显示出在自然语言任务上表现良好。许多先前的作品都以通过知识图(KGS)标记的关系链接的实体的形式利用结构性常识来协助PTLM。检索方法使用kg作为单独的静态模块,该模块限制了覆盖范围,因为kgs包含有限的知识。生成方法训练PTLMS kg三倍以提高获得知识的规模。但是,对符号KG实体的培训限制了其在涉及自然语言文本的任务中的适用性,在这些任务中,它们忽略了整体上下文。为了减轻这种情况,我们提出了一个以句子为条件的常识性上下文化器(COSE-CO)作为输入,以使其在生成与输入文本的整体上下文相关的任务中通常可用。为了训练Cose-Co,我们提出了一个新的数据集,其中包括句子和常识知识对。 COSE-CO推断出的知识是多种多样的,并且包含了基础KG中不存在的新实体。我们增强了在多选质量质量检查和开放式常识性推理任务中产生的知识,从而改善了CSQA,ARC,QASC和OBQA数据集的当前最佳方法。我们还展示了其在改善释义生成任务的基线模型方面的适用性。
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Structured tabular data exist across nearly all fields. Reasoning task over these data aims to answer questions or determine the truthiness of hypothesis sentences by understanding the semantic meaning of a table. While previous works have devoted significant efforts to the tabular reasoning task, they always assume there are sufficient labeled data. However, constructing reasoning samples over tables (and related text) is labor-intensive, especially when the reasoning process is complex. When labeled data is insufficient, the performance of models will suffer an unendurable decline. In this paper, we propose a unified framework for unsupervised complex tabular reasoning (UCTR), which generates sufficient and diverse synthetic data with complex logic for tabular reasoning tasks, assuming no human-annotated data at all. We first utilize a random sampling strategy to collect diverse programs of different types and execute them on tables based on a "Program-Executor" module. To bridge the gap between the programs and natural language sentences, we design a powerful "NL-Generator" module to generate natural language sentences with complex logic from these programs. Since a table often occurs with its surrounding texts, we further propose novel "Table-to-Text" and "Text-to-Table" operators to handle joint table-text reasoning scenarios. This way, we can adequately exploit the unlabeled table resources to obtain a well-performed reasoning model under an unsupervised setting. Our experiments cover different tasks (question answering and fact verification) and different domains (general and specific), showing that our unsupervised methods can achieve at most 93% performance compared to supervised models. We also find that it can substantially boost the supervised performance in low-resourced domains as a data augmentation technique. Our code is available at https://github.com/leezythu/UCTR.
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大型语言模型在各种任务上显示出令人印象深刻的几次结果。但是,当知识是此类结果的关键时,就像问题回答和事实检查之类的任务一样,似乎需要存储知识的大量参数计数。众所周知,检索增强模型可以在不需要多个参数的情况下在知识密集的任务上表现出色,但是目前尚不清楚它们是否在几个弹药设置中工作。在这项工作中,我们介绍了地图集,这是一个经过精心设计和预先训练的增强语言模型,能够通过很少的培训示例学习知识密集型任务。我们对包括MMLU,苏格兰短裙和归类等各种任务进行评估,并研究文档索引内容的影响,表明它可以很容易地进行更新。值得注意的是,在自然问题上仅使用64个示例在自然问题上达到超过42 \%的准确性,尽管参数少了50倍,但比540B参数模型的表现优于540b参数模型。
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A key component of fact verification is thevevidence retrieval, often from multiple documents. Recent approaches use dense representations and condition the retrieval of each document on the previously retrieved ones. The latter step is performed over all the documents in the collection, requiring storing their dense representations in an index, thus incurring a high memory footprint. An alternative paradigm is retrieve-and-rerank, where documents are retrieved using methods such as BM25, their sentences are reranked, and further documents are retrieved conditioned on these sentences, reducing the memory requirements. However, such approaches can be brittle as they rely on heuristics and assume hyperlinks between documents. We propose a novel retrieve-and-rerank method for multi-hop retrieval, that consists of a retriever that jointly scores documents in the knowledge source and sentences from previously retrieved documents using an autoregressive formulation and is guided by a proof system based on natural logic that dynamically terminates the retrieval process if the evidence is deemed sufficient. This method is competitive with current state-of-the-art methods on FEVER, HoVer and FEVEROUS-S, while using $5$ to $10$ times less memory than competing systems. Evaluation on an adversarial dataset indicates improved stability of our approach compared to commonly deployed threshold-based methods. Finally, the proof system helps humans predict model decisions correctly more often than using the evidence alone.
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In this paper we introduce a new publicly available dataset for verification against textual sources, FEVER: Fact Extraction and VERification. It consists of 185,445 claims generated by altering sentences extracted from Wikipedia and subsequently verified without knowledge of the sentence they were derived from.The claims are classified as SUPPORTED, RE-FUTED or NOTENOUGHINFO by annotators achieving 0.6841 in Fleiss κ. For the first two classes, the annotators also recorded the sentence(s) forming the necessary evidence for their judgment. To characterize the challenge of the dataset presented, we develop a pipeline approach and compare it to suitably designed oracles. The best accuracy we achieve on labeling a claim accompanied by the correct evidence is 31.87%, while if we ignore the evidence we achieve 50.91%. Thus we believe that FEVER is a challenging testbed that will help stimulate progress on claim verification against textual sources.
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Deep Learning and Machine Learning based models have become extremely popular in text processing and information retrieval. However, the non-linear structures present inside the networks make these models largely inscrutable. A significant body of research has focused on increasing the transparency of these models. This article provides a broad overview of research on the explainability and interpretability of natural language processing and information retrieval methods. More specifically, we survey approaches that have been applied to explain word embeddings, sequence modeling, attention modules, transformers, BERT, and document ranking. The concluding section suggests some possible directions for future research on this topic.
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Transformer-based models have pushed state of the art in many areas of NLP, but our understanding of what is behind their success is still limited. This paper is the first survey of over 150 studies of the popular BERT model. We review the current state of knowledge about how BERT works, what kind of information it learns and how it is represented, common modifications to its training objectives and architecture, the overparameterization issue and approaches to compression. We then outline directions for future research.
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We present LogiGAN, an unsupervised adversarial pre-training framework for improving logical reasoning abilities of language models. Upon automatic identifying logical reasoning phenomena in massive text corpus via detection heuristics, we train language models to predict the masked-out logical statements. Inspired by the facilitation effect of reflective thinking in human learning, we analogically simulate the learning-thinking process with an adversarial Generator-Verifier architecture to assist logic learning. LogiGAN implements a novel sequential GAN approach that (a) circumvents the non-differentiable challenge of the sequential GAN by leveraging the Generator as a sentence-level generative likelihood scorer with a learning objective of reaching scoring consensus with the Verifier; (b) is computationally feasible for large-scale pre-training with arbitrary target length. Both base and large size language models pre-trained with LogiGAN demonstrate obvious performance improvement on 12 datasets requiring general reasoning abilities, revealing the fundamental role of logic in broad reasoning, as well as the effectiveness of LogiGAN. Ablation studies on LogiGAN components reveal the relative orthogonality between linguistic and logic abilities and suggest that reflective thinking's facilitation effect might also generalize to machine learning.
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命名实体识别(ner)是从文本中提取特定类型的命名实体的任务。当前的NER模型往往依赖于人类注释的数据集,要求在目标领域和实体上广泛参与专业知识。这项工作介绍了一个询问生成的方法,它通过询问反映实体类型的需求的简单自然语言问题来自动生成NER数据集(例如,哪种疾病?)到开放式域问题应答系统。不使用任何域中资源(即,培训句子,标签或域名词典),我们的模型在我们生成的数据集上仅培训了,这在很大程度上超过了四个不同域的六个基准测试的弱势监督模型。令人惊讶的是,在NCBI疾病中,我们的模型达到75.5 F1得分,甚至优于以前的最佳弱监督模型4.1 F1得分,它利用域专家提供的丰富的域名词典。制定具有自然语言的NER的需求,也允许我们为诸如奖项等细粒度实体类型构建NER模型,其中我们的模型甚至优于完全监督模型。在三个少量的NER基准测试中,我们的模型实现了新的最先进的性能。
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