One of the common traits of past and present approaches for Semantic Role Labeling (SRL) is that they rely upon discrete labels drawn from a predefined linguistic inventory to classify predicate senses and their arguments. However, we argue this need not be the case. In this paper, we present an approach that leverages Definition Modeling to introduce a generalized formulation of SRL as the task of describing predicate-argument structures using natural language definitions instead of discrete labels. Our novel formulation takes a first step towards placing interpretability and flexibility foremost, and yet our experiments and analyses on PropBank-style and FrameNet-style, dependency-based and span-based SRL also demonstrate that a flexible model with an interpretable output does not necessarily come at the expense of performance. We release our software for research purposes at https://github.com/SapienzaNLP/dsrl.

语义角色标签（SRL）旨在识别句子的谓词题目结构，并可以分解为两个子任务：谓词歧义歧义和参数标记。先前的工作独立处理这两个任务，这两个任务忽略了两个任务之间的语义连接。在本文中，我们建议使用机器阅读理解（MRC）框架来弥合这一差距。我们将谓词歧义形式化为多项选择的机器阅读理解，其中给定谓词的候选感官的描述用作选择正确的感觉的选项。然后使用所选的谓词感来确定该谓词的语义角色，这些语义角色用于构建另一个MRC模型的查询以进行参数标记。这样，我们能够利用参数标记的谓词语义和语义角色语义。我们还建议为计算效率选择所有可能的语义角色的子集。实验表明，所提出的框架可实现与先前工作的最新结果或可比的结果。代码可在\ url {https://github.com/shannonai/mrc-srl}上获得。

数据饥饿的深度神经网络已经将自己作为许多NLP任务的标准建立为包括传统序列标记的标准。尽管他们在高资源语言上表现最先进的表现，但它们仍然落后于低资源场景的统计计数器。一个方法来反击攻击此问题是文本增强，即，从现有数据生成新的合成训练数据点。虽然NLP最近目睹了一种文本增强技术的负载，但该领域仍然缺乏对多种语言和序列标记任务的系统性能分析。为了填补这一差距，我们调查了三类文本增强方法，其在语法（例如，裁剪子句子），令牌（例如，随机字插入）和字符（例如，字符交换）级别上执行更改。我们系统地将它们与语音标记，依赖解析和语义角色标记的分组进行了比较，用于使用各种模型的各种语言系列，包括依赖于诸如MBERT的普赖金的多语言语境化语言模型的架构。增强最显着改善了解析，然后是语音标记和语义角色标记的依赖性解析。我们发现实验技术通常在形态上丰富的语言，而不是越南语等分析语言。我们的研究结果表明，增强技术可以进一步改善基于MBERT的强基线。我们将字符级方法标识为最常见的表演者，而同义词替换和语法增强仪提供不一致的改进。最后，我们讨论了最大依赖于任务，语言对和模型类型的结果。

我们提出了一个新的框架，在增强的自然语言（TANL）之间的翻译，解决了许多结构化预测语言任务，包括联合实体和关系提取，嵌套命名实体识别，关系分类，语义角色标记，事件提取，COREREFED分辨率和对话状态追踪。通过培训特定于特定于任务的鉴别分类器来说，我们将其作为一种在增强的自然语言之间的翻译任务，而不是通过培训问题，而不是解决问题，而是可以轻松提取任务相关信息。我们的方法可以匹配或优于所有任务的特定于任务特定模型，特别是在联合实体和关系提取（Conll04，Ade，NYT和ACE2005数据集）上实现了新的最先进的结果，与关系分类（偶尔和默示）和语义角色标签（Conll-2005和Conll-2012）。我们在使用相同的架构和超参数的同时为所有任务使用相同的架构和超级参数，甚至在培训单个模型时同时解决所有任务（多任务学习）。最后，我们表明，由于更好地利用标签语义，我们的框架也可以显着提高低资源制度的性能。

Question Generation (QG) is fundamentally a simple syntactic transformation; however, many aspects of semantics influence what questions are good to form. We implement this observation by developing SynQG, a set of transparent syntactic rules leveraging universal dependencies, shallow semantic parsing, lexical resources, and custom rules which transform declarative sentences into question-answer pairs. We utilize PropBank argument descriptions and VerbNet state predicates to incorporate shallow semantic content, which helps generate questions of a descriptive nature and produce inferential and semantically richer questions than existing systems. In order to improve syntactic fluency and eliminate grammatically incorrect questions, we employ back-translation over the output of these syntactic rules. A set of crowd-sourced evaluations shows that our system can generate a larger number of highly grammatical and relevant questions than previous QG systems and that back-translation drastically improves grammaticality at a slight cost of generating irrelevant questions.

For natural language understanding (NLU) technology to be maximally useful, it must be able to process language in a way that is not exclusive to a single task, genre, or dataset. In pursuit of this objective, we introduce the General Language Understanding Evaluation (GLUE) benchmark, a collection of tools for evaluating the performance of models across a diverse set of existing NLU tasks. By including tasks with limited training data, GLUE is designed to favor and encourage models that share general linguistic knowledge across tasks. GLUE also includes a hand-crafted diagnostic test suite that enables detailed linguistic analysis of models. We evaluate baselines based on current methods for transfer and representation learning and find that multi-task training on all tasks performs better than training a separate model per task. However, the low absolute performance of our best model indicates the need for improved general NLU systems.

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}）。

The widely studied task of Natural Language Inference (NLI) requires a system to recognize whether one piece of text is textually entailed by another, i.e. whether the entirety of its meaning can be inferred from the other. In current NLI datasets and models, textual entailment relations are typically defined on the sentence- or paragraph-level. However, even a simple sentence often contains multiple propositions, i.e. distinct units of meaning conveyed by the sentence. As these propositions can carry different truth values in the context of a given premise, we argue for the need to recognize the textual entailment relation of each proposition in a sentence individually. We propose PropSegmEnt, a corpus of over 35K propositions annotated by expert human raters. Our dataset structure resembles the tasks of (1) segmenting sentences within a document to the set of propositions, and (2) classifying the entailment relation of each proposition with respect to a different yet topically-aligned document, i.e. documents describing the same event or entity. We establish strong baselines for the segmentation and entailment tasks. Through case studies on summary hallucination detection and document-level NLI, we demonstrate that our conceptual framework is potentially useful for understanding and explaining the compositionality of NLI labels.

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.

Frame Semantic Role Labeling (FSRL) identifies arguments and labels them with frame semantic roles defined in FrameNet. Previous researches tend to divide FSRL into argument identification and role classification. Such methods usually model role classification as naive multi-class classification and treat arguments individually, which neglects label semantics and interactions between arguments and thus hindering performance and generalization of models. In this paper, we propose a query-based framework named ArGument Extractor with Definitions in FrameNet (AGED) to mitigate these problems. Definitions of frames and frame elements (FEs) in FrameNet can be used to query arguments in text. Encoding text-definition pairs can guide models in learning label semantics and strengthening argument interactions. Experiments show that AGED outperforms previous state-of-the-art by up to 1.3 F1-score in two FrameNet datasets and the generalization power of AGED in zero-shot and fewshot scenarios. Our code and technical appendix is available at https://github.com/PKUnlp-icler/AGED.

开放信息提取（OpenIE）的最先进的神经方法通常以自回旋或基于谓词的方式迭代地提取三重态（或元组），以免产生重复。在这项工作中，我们提出了一种可以平等或更成功的问题的不同方法。也就是说，我们提出了一种新型的单通道方法，用于开放式启发，该方法受到计算机视觉的对象检测算法的启发。我们使用基于双方匹配的订单不足损失，迫使独特的预测和用于序列标签的仅基于变压器的纯编码体系结构。与质量指标和推理时间相比，与标准基准的最新模型相比，提出的方法更快，并且表现出卓越或类似的性能。我们的模型在CARB上的新最新性能为OIE2016评估，而推断的速度比以前的最新状态更快。我们还在两种语言的零弹奏设置中评估了模型的多语言版本，并引入了一种生成合成多语言数据的策略，以微调每个特定语言的模型。在这种情况下，我们在多语言Re-OIE2016上显示了15％的性能提高，葡萄牙语和西班牙语的F1达到75％。代码和型号可在https://github.com/sberbank-ai/detie上找到。

We propose a unified neural network architecture and learning algorithm that can be applied to various natural language processing tasks including: part-of-speech tagging, chunking, named entity recognition, and semantic role labeling. This versatility is achieved by trying to avoid task-specific engineering and therefore disregarding a lot of prior knowledge. Instead of exploiting man-made input features carefully optimized for each task, our system learns internal representations on the basis of vast amounts of mostly unlabeled training data. This work is then used as a basis for building a freely available tagging system with good performance and minimal computational requirements.

开放信息提取（OpenIE）促进了独立于域的大型语料库的关系事实的发现。该技术很好地适合许多开放世界的自然语言理解场景，例如自动知识基础构建，开放域问答和明确的推理。由于深度学习技术的快速发展，已经提出了许多神经开放式体系结构并取得了可观的性能。在这项调查中，我们提供了有关状态神经开放模型的广泛概述，其关键设计决策，优势和劣势。然后，我们讨论当前解决方案的局限性以及OpenIE问题本身的开放问题。最后，我们列出了最近的趋势，这些趋势可以帮助扩大其范围和适用性，从而为Openie的未来研究设定了有希望的方向。据我们所知，本文是有关此特定主题的第一篇评论。

Translating training data into many languages has emerged as a practical solution for improving cross-lingual transfer. For tasks that involve span-level annotations, such as information extraction or question answering, an additional label projection step is required to map annotated spans onto the translated texts. Recently, a few efforts have utilized a simple mark-then-translate method to jointly perform translation and projection by inserting special markers around the labeled spans in the original sentence. However, as far as we are aware, no empirical analysis has been conducted on how this approach compares to traditional annotation projection based on word alignment. In this paper, we present an extensive empirical study across 42 languages and three tasks (QA, NER, and Event Extraction) to evaluate the effectiveness and limitations of both methods, filling an important gap in the literature. Experimental results show that our optimized version of mark-then-translate, which we call EasyProject, is easily applied to many languages and works surprisingly well, outperforming the more complex word alignment-based methods. We analyze several key factors that affect end-task performance, and show EasyProject works well because it can accurately preserve label span boundaries after translation. We will publicly release all our code and data.

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。

许多自然语言处理任务，例如核心解决方案和语义角色标签，都需要选择文本跨度并就其做出决定。此类任务的典型方法是为所有可能的跨度评分，并贪婪地选择特定任务的下游处理的跨度。然而，这种方法并未纳入有关应选择哪种跨度的诱导偏见，例如，选定的跨度倾向于是句法成分。在本文中，我们提出了一种新型的基于语法的结构化选择模型，该模型学会了利用为此类问题提供的部分跨度注释。与以前的方法相比，我们的方法摆脱了启发式贪婪的跨度选择方案，使我们能够在一组最佳跨度上对下游任务进行建模。我们在两个流行的跨度预测任务上评估我们的模型：核心分辨率和语义角色标签。我们对两者都展示了经验改进。

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.

开放信息提取是一个重要的NLP任务，它针对从非结构化文本中提取结构化信息的目标，而无需限制关系类型或文本域。该调查文件涵盖了2007年至2022年的开放信息提取技术，重点是以前的调查未涵盖的新模型。我们从信息角度来源提出了一种新的分类方法，以适应最近的OIE技术的开发。此外，我们根据任务设置以及当前流行的数据集和模型评估指标总结了三种主要方法。鉴于全面的审查，从数据集，信息来源，输出表格，方法和评估指标方面显示了几个未来的方向。

As machine translation (MT) metrics improve their correlation with human judgement every year, it is crucial to understand the limitations of such metrics at the segment level. Specifically, it is important to investigate metric behaviour when facing accuracy errors in MT because these can have dangerous consequences in certain contexts (e.g., legal, medical). We curate ACES, a translation accuracy challenge set, consisting of 68 phenomena ranging from simple perturbations at the word/character level to more complex errors based on discourse and real-world knowledge. We use ACES to evaluate a wide range of MT metrics including the submissions to the WMT 2022 metrics shared task and perform several analyses leading to general recommendations for metric developers. We recommend: a) combining metrics with different strengths, b) developing metrics that give more weight to the source and less to surface-level overlap with the reference and c) explicitly modelling additional language-specific information beyond what is available via multilingual embeddings.

方面含义是指如何提出情况的内部时间结构。这包括情况是将情况描述为状态还是事件，无论情况已经完成还是正在进行，以及是否被视为一个整体，还是关注特定阶段。这项调查概述了对词汇和语法方面进行建模以及对必要语言概念和术语的直观解释的概述。特别是，我们描述了统计，远程感，习惯性，完美和不完美的概念，以及最终性和情况类型的有影响力的清单。我们认为，由于方面是语义的关键组成部分，尤其是在以精确的方式报告情况的时间结构时，未来的NLP方法需要能够系统地处理和评估它，以实现人类水平的语言理解。