基于变压器的语言模型最近在许多自然语言任务中取得了显着的结果。但是,通常通过利用大量培训数据来实现排行榜的性能,并且很少通过将明确的语言知识编码为神经模型。这使许多人质疑语言学对现代自然语言处理的相关性。在本文中,我介绍了几个案例研究,以说明理论语言学和神经语言模型仍然相互关联。首先,语言模型通过提供一个客观的工具来测量语义距离,这对语言学家很有用,语义距离很难使用传统方法。另一方面,语言理论通过提供框架和数据源来探究我们的语言模型,以了解语言理解的特定方面,从而有助于语言建模研究。本论文贡献了三项研究,探讨了语言模型中语法 - 听觉界面的不同方面。在论文的第一部分中,我将语言模型应用于单词类灵活性的问题。我将Mbert作为语义距离测量的来源,我提供了有利于将单词类灵活性分析为方向过程的证据。在论文的第二部分中,我提出了一种方法来测量语言模型中间层的惊奇方法。我的实验表明,包含形态句法异常的句子触发了语言模型早期的惊喜,而不是语义和常识异常。最后,在论文的第三部分中,我适应了一些心理语言学研究,以表明语言模型包含了论证结构结构的知识。总而言之,我的论文在自然语言处理,语言理论和心理语言学之间建立了新的联系,以为语言模型的解释提供新的观点。
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自然语言处理的机器学习快速进步有可能改变有关人类学习语言的辩论。但是,当前人工学习者和人类的学习环境和偏见以削弱从学习模拟获得的证据的影响的方式分歧。例如,当今最有效的神经语言模型接受了典型儿童可用的语言数据量的大约一千倍。为了增加计算模型的可学习性结果的相关性,我们需要培训模型学习者,而没有比人类具有显着优势的学习者。如果合适的模型成功地获得了一些目标语言知识,则可以提供一个概念证明,即在假设的人类学习方案中可以学习目标。合理的模型学习者将使我们能够进行实验操作,以对学习环境中的变量进行因果推断,并严格测试史密斯风格的贫困声明,主张根据人类对人类的先天语言知识,基于有关可学习性的猜测。由于实用和道德的考虑因素,人类受试者将永远无法实现可比的实验,从而使模型学习者成为必不可少的资源。到目前为止,试图剥夺当前模型的不公平优势,为关键语法行为(例如可接受性判断)获得亚人类结果。但是,在我们可以合理地得出结论,语言学习需要比当前模型拥有更多的特定领域知识,我们必须首先以多模式刺激和多代理互动的形式探索非语言意见,以使学习者更有效地学习学习者来自有限的语言输入。
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我们研究了现代神经语言模型容易受到结构启动的程度,这种现象使句子的结构在后续句子中更有可能使相同的结构更有可能。我们探索如何使用启动来研究这些模型学习抽象结构信息的潜力,这是需要自然语言理解技能的任务良好表现的先决条件。我们引入了一种新型的度量标准和释放Prime-LM,这是一个大型语料库,我们可以控制与启动强度相互作用的各种语言因素。我们发现,变压器模型确实显示了结构启动的证据,但他们所学到的概括在某种程度上是由语义信息调节的。我们的实验还表明,模型获得的表示不仅可以编码抽象的顺序结构,而且还涉及一定级别的层次句法信息。更普遍的是,我们的研究表明,启动范式是一种有用的,可用于洞悉语言模型能力的有用的,并为未来的基于底漆的调查打开了探测模型内部状态的未来大门。
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People constantly use language to learn about the world. Computational linguists have capitalized on this fact to build large language models (LLMs) that acquire co-occurrence-based knowledge from language corpora. LLMs achieve impressive performance on many tasks, but the robustness of their world knowledge has been questioned. Here, we ask: do LLMs acquire generalized knowledge about real-world events? Using curated sets of minimal sentence pairs (n=1215), we tested whether LLMs are more likely to generate plausible event descriptions compared to their implausible counterparts. We found that LLMs systematically distinguish possible and impossible events (The teacher bought the laptop vs. The laptop bought the teacher) but fall short of human performance when distinguishing likely and unlikely events (The nanny tutored the boy vs. The boy tutored the nanny). In follow-up analyses, we show that (i) LLM scores are driven by both plausibility and surface-level sentence features, (ii) LLMs generalize well across syntactic sentence variants (active vs passive) but less well across semantic sentence variants (synonymous sentences), (iii) some, but not all LLM deviations from ground-truth labels align with crowdsourced human judgments, and (iv) explicit event plausibility information emerges in middle LLM layers and remains high thereafter. Overall, our analyses reveal a gap in LLMs' event knowledge, highlighting their limitations as generalized knowledge bases. We conclude by speculating that the differential performance on impossible vs. unlikely events is not a temporary setback but an inherent property of LLMs, reflecting a fundamental difference between linguistic knowledge and world knowledge in intelligent systems.
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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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This paper investigates the ability of artificial neural networks to judge the grammatical acceptability of a sentence, with the goal of testing their linguistic competence. We introduce the Corpus of Linguistic Acceptability (CoLA), a set of 10,657 English sentences labeled as grammatical or ungrammatical from published linguistics literature. As baselines, we train several recurrent neural network models on acceptability classification, and find that our models outperform unsupervised models by Lau et al. (2016) on CoLA. Error-analysis on specific grammatical phenomena reveals that both Lau et al.'s models and ours learn systematic generalizations like subject-verb-object order. However, all models we test perform far below human level on a wide range of grammatical constructions.
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在NLP社区中有一个正在进行的辩论,无论现代语言模型是否包含语言知识,通过所谓的探针恢复。在本文中,我们研究了语言知识是否是现代语言模型良好表现的必要条件,我们称之为\ Texit {重新发现假设}。首先,我们展示了语言模型,这是显着压缩的,但在预先磨普目标上表现良好,以便在语言结构探讨时保持良好的分数。这一结果支持重新发现的假设,并导致我们的论文的第二款贡献:一个信息 - 理论框架,与语言建模目标相关。该框架还提供了测量语言信息对字词预测任务的影响的度量标准。我们通过英语综合和真正的NLP任务加固我们的分析结果。
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当前的语言模型可以产生高质量的文本。他们只是复制他们之前看到的文本,或者他们学习了普遍的语言抽象吗?要取笑这些可能性,我们介绍了乌鸦,这是一套评估生成文本的新颖性,专注于顺序结构(n-gram)和句法结构。我们将这些分析应用于四种神经语言模型(LSTM,变压器,变换器-XL和GPT-2)。对于本地结构 - 例如,单个依赖性 - 模型生成的文本比来自每个模型的测试集的人类生成文本的基线显着不那么新颖。对于大规模结构 - 例如,总句结构 - 模型生成的文本与人生成的基线一样新颖甚至更新颖,但模型仍然有时复制,在某些情况下,在训练集中重复超过1000字超过1,000字的通道。我们还表现了广泛的手动分析,表明GPT-2的新文本通常在形态学和语法中形成良好,但具有合理的语义问题(例如,是自相矛盾)。
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Grammatical Error Correction (GEC) is the task of automatically detecting and correcting errors in text. The task not only includes the correction of grammatical errors, such as missing prepositions and mismatched subject-verb agreement, but also orthographic and semantic errors, such as misspellings and word choice errors respectively. The field has seen significant progress in the last decade, motivated in part by a series of five shared tasks, which drove the development of rule-based methods, statistical classifiers, statistical machine translation, and finally neural machine translation systems which represent the current dominant state of the art. In this survey paper, we condense the field into a single article and first outline some of the linguistic challenges of the task, introduce the most popular datasets that are available to researchers (for both English and other languages), and summarise the various methods and techniques that have been developed with a particular focus on artificial error generation. We next describe the many different approaches to evaluation as well as concerns surrounding metric reliability, especially in relation to subjective human judgements, before concluding with an overview of recent progress and suggestions for future work and remaining challenges. We hope that this survey will serve as comprehensive resource for researchers who are new to the field or who want to be kept apprised of recent developments.
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语言可以用作再现和执行有害刻板印象和偏差的手段,并被分析在许多研究中。在本文中,我们对自然语言处理中的性别偏见进行了304篇论文。我们分析了社会科学中性别及其类别的定义,并将其连接到NLP研究中性别偏见的正式定义。我们调查了在对性别偏见的研究中应用的Lexica和数据集,然后比较和对比方法来检测和减轻性别偏见。我们发现对性别偏见的研究遭受了四个核心限制。 1)大多数研究将性别视为忽视其流动性和连续性的二元变量。 2)大部分工作都在单机设置中进行英语或其他高资源语言进行。 3)尽管在NLP方法中对性别偏见进行了无数的论文,但我们发现大多数新开发的算法都没有测试他们的偏见模型,并无视他们的工作的伦理考虑。 4)最后,在这一研究线上发展的方法基本缺陷涵盖性别偏差的非常有限的定义,缺乏评估基线和管道。我们建议建议克服这些限制作为未来研究的指导。
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在本文中,我们试图通过引入深度学习模型的句法归纳偏见来建立两所学校之间的联系。我们提出了两个归纳偏见的家族,一个家庭用于选区结构,另一个用于依赖性结构。选区归纳偏见鼓励深度学习模型使用不同的单位(或神经元)分别处理长期和短期信息。这种分离为深度学习模型提供了一种方法,可以从顺序输入中构建潜在的层次表示形式,即更高级别的表示由高级表示形式组成,并且可以分解为一系列低级表示。例如,在不了解地面实际结构的情况下,我们提出的模型学会通过根据其句法结构组成变量和运算符的表示来处理逻辑表达。另一方面,依赖归纳偏置鼓励模型在输入序列中找到实体之间的潜在关系。对于自然语言,潜在关系通常被建模为一个定向依赖图,其中一个单词恰好具有一个父节点和零或几个孩子的节点。将此约束应用于类似变压器的模型之后,我们发现该模型能够诱导接近人类专家注释的有向图,并且在不同任务上也优于标准变压器模型。我们认为,这些实验结果为深度学习模型的未来发展展示了一个有趣的选择。
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本次调查绘制了用于分析社交媒体数据的生成方法的研究状态的广泛的全景照片(Sota)。它填补了空白,因为现有的调查文章在其范围内或被约会。我们包括两个重要方面,目前正在挖掘和建模社交媒体的重要性:动态和网络。社会动态对于了解影响影响或疾病的传播,友谊的形成,友谊的形成等,另一方面,可以捕获各种复杂关系,提供额外的洞察力和识别否则将不会被注意的重要模式。
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The rapid advancement of AI technology has made text generation tools like GPT-3 and ChatGPT increasingly accessible, scalable, and effective. This can pose serious threat to the credibility of various forms of media if these technologies are used for plagiarism, including scientific literature and news sources. Despite the development of automated methods for paraphrase identification, detecting this type of plagiarism remains a challenge due to the disparate nature of the datasets on which these methods are trained. In this study, we review traditional and current approaches to paraphrase identification and propose a refined typology of paraphrases. We also investigate how this typology is represented in popular datasets and how under-representation of certain types of paraphrases impacts detection capabilities. Finally, we outline new directions for future research and datasets in the pursuit of more effective paraphrase detection using AI.
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虽然句子异常已经定期应用于NLP中的测试,但我们尚未建立从NLP模型中的表示中的异常信息的确切状态的图片。在本文中,我们的目标是填补两个主要间隙,重点关注句法异常的领域。首先,我们通过设计改变异常在句子中发生的分层级别的探测任务来探讨异常编码的细粒度差异。其次,我们不仅测试了模型能够通过检查不同异常类型之间的转移来检测给定异常的能力,还能检测给定的异常信号的一般性。结果表明,所有型号都编码一些支持异常检测的信息,但检测性能在异常之间变化,并且只有最近的变压器模型的唯一表示显示了异常知识的概括知识的迹象。随访分析支持这些模型在合法的句子奇迹上接受合法的概念,而粗糙的单词位置信息也可能是观察到的异常检测的贡献者。
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多语言语言模型(\ mllms),如mbert,xlm,xlm-r,\ textit {etc。}已成为一种可行的选择,使预先估计到大量语言的力量。鉴于他们的成功在零射击转移学习中,在(i)建立更大的\ mllms〜覆盖了大量语言(ii)创建覆盖更广泛的任务和语言来评估的详尽工作基准mllms〜(iii)分析单音零点,零拍摄交叉和双语任务(iv)对Monolingual的性能,了解\ mllms〜(v)增强(通常)学习的通用语言模式(如果有的话)有限的容量\ mllms〜以提高他们在已见甚至看不见语言的表现。在这项调查中,我们审查了现有的文学,涵盖了上述与\ MLLMS有关的广泛研究领域。根据我们的调查,我们建议您有一些未来的研究方向。
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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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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})。
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Contextualized representation models such as ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2018) have recently achieved state-of-the-art results on a diverse array of downstream NLP tasks. Building on recent token-level probing work, we introduce a novel edge probing task design and construct a broad suite of sub-sentence tasks derived from the traditional structured NLP pipeline. We probe word-level contextual representations from four recent models and investigate how they encode sentence structure across a range of syntactic, semantic, local, and long-range phenomena. We find that existing models trained on language modeling and translation produce strong representations for syntactic phenomena, but only offer comparably small improvements on semantic tasks over a non-contextual baseline.
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Winograd架构挑战 - 一套涉及代词参考消歧的双句话,似乎需要使用致辞知识 - 是由2011年的赫克托勒维克斯提出的。到2019年,基于大型预先训练的变压器的一些AI系统基于语言模型和微调这些问题,精度优于90%。在本文中,我们审查了Winograd架构挑战的历史并评估了其重要性。
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The long-distance agreement, evidence for syntactic structure, is increasingly used to assess the syntactic generalization of Neural Language Models. Much work has shown that transformers are capable of high accuracy in varied agreement tasks, but the mechanisms by which the models accomplish this behavior are still not well understood. To better understand transformers' internal working, this work contrasts how they handle two superficially similar but theoretically distinct agreement phenomena: subject-verb and object-past participle agreement in French. Using probing and counterfactual analysis methods, our experiments show that i) the agreement task suffers from several confounders which partially question the conclusions drawn so far and ii) transformers handle subject-verb and object-past participle agreements in a way that is consistent with their modeling in theoretical linguistics.
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