两个关键假设塑造了排名检索的通常视图:(1)搜索者可以为他们希望看到的文档中的疑问选择单词,并且(2)排名检索的文档就足以,因为搜索者将足够就足够了能够认识到他们希望找到的那些。当要搜索的文档处于搜索者未知的语言时,既不是真的。在这种情况下,需要跨语言信息检索(CLIR)。本章审查了艺术技术的交流信息检索,并概述了一些开放的研究问题。
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在这项工作中,我们提出了一个系统的实证研究,专注于最先进的多语言编码器在跨越多种不同语言对的交叉语言文档和句子检索任务的适用性。我们首先将这些模型视为多语言文本编码器,并在无监督的ad-hoc句子和文档级CLIR中基准性能。与监督语言理解相比,我们的结果表明,对于无监督的文档级CLIR - 一个没有针对IR特定的微调 - 预训练的多语言编码器的相关性判断,平均未能基于CLWE显着优于早期模型。对于句子级检索,我们确实获得了最先进的性能:然而,通过多语言编码器来满足高峰分数,这些编码器已经进一步专注于监督的时尚,以便句子理解任务,而不是使用他们的香草'现货'变体。在这些结果之后,我们介绍了文档级CLIR的本地化相关性匹配,在那里我们独立地对文件部分进行了查询。在第二部分中,我们评估了在一系列零拍语言和域转移CLIR实验中的英语相关数据中进行微调的微调编码器精细调整的微调我们的结果表明,监督重新排名很少提高多语言变压器作为无监督的基数。最后,只有在域名对比度微调(即,同一域名,只有语言转移),我们设法提高排名质量。我们在目标语言中单次检索的交叉定向检索结果和结果(零拍摄)交叉传输之间的显着实证差异,这指出了在单机数据上训练的检索模型的“单声道过度装备”。
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The word alignment task, despite its prominence in the era of statistical machine translation (SMT), is niche and under-explored today. In this two-part tutorial, we argue for the continued relevance for word alignment. The first part provides a historical background to word alignment as a core component of the traditional SMT pipeline. We zero-in on GIZA++, an unsupervised, statistical word aligner with surprising longevity. Jumping forward to the era of neural machine translation (NMT), we show how insights from word alignment inspired the attention mechanism fundamental to present-day NMT. The second part shifts to a survey approach. We cover neural word aligners, showing the slow but steady progress towards surpassing GIZA++ performance. Finally, we cover the present-day applications of word alignment, from cross-lingual annotation projection, to improving translation.
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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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本文概述了了解信息检索和自然语言处理中最近的发展的概念框架,试图集成密集和稀疏检索方法。我提出了一种代表性方法,将核心文本检索问题与逻辑评分模型和物理检索模型中断。评分模型在编码器方面定义,将查询和文档映射到代表空间,以及计算查询文档分数的比较函数。物理检索模型定义了系统如何从关于查询的任意大语料库产生顶级k $ Scoring文档。分别沿两个维度进一步分析得分模型:密集与稀疏表示和监督(学习)与无监督的方法。我展示了许多最近提出的检索方法,包括多级排名设计,可以看作是本框架中的不同参数化,并且统一视图表明了许多开放的研究问题,为未来的工作提供了路线图。作为奖金,这种概念框架在计算时建立了与自然语言处理和信息访问“技术”中的句子相似任务的连接。
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Colbert-X是跨语言信息检索(CLIR)的密集检索模型。在克里尔(Clir)中,文档是用一种自然语言编写的,而查询则以另一种语言表示。相关任务是多语言IR(MLIR),该系统在其中创建了以多种语言编写的单个文档列表。鉴于Colbert-X依赖于预审慎的多语言神经语言模型对文档进行排名,因此,多语言培训程序可以使Colbert-X版本适合MLIR。本文描述了培训程序。良好MLIR排名的一个重要因素是使用混合语言批次进行微调XLM-R,其中相同的查询与同一批次中不同语言的文档匹配。MS MARCO通道的神经机器翻译用于微调模型。
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识别跨语言抄袭是挑战性的,特别是对于遥远的语言对和感知翻译。我们介绍了这项任务的新型多语言检索模型跨语言本体论(CL \ nobreakdash-osa)。 CL-OSA表示从开放知识图Wikidata获得的实体向量的文档。反对其他方法,Cl \ nobreakdash-osa不需要计算昂贵的机器翻译,也不需要使用可比较或平行语料库进行预培训。它可靠地歧义同音异义和缩放,以允许其应用于Web级文档集合。我们展示了CL-OSA优于从五个大局部多样化的测试语料中检索候选文档的最先进的方法,包括日语英语等遥控语言对。为了识别在角色级别的跨语言抄袭,CL-OSA主要改善了感觉识别翻译的检测。对于这些挑战性案例,CL-OSA在良好的Plagdet得分方面的表现超过了最佳竞争对手的比例超过两种。我们研究的代码和数据公开可用。
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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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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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在本文中,我们分享了我们努力建立能够翻译一千多种语言的实用机器翻译(MT)系统的发现。我们在三个研究领域中描述了结果:(i)通过利用半监督预训练的语言识别和开发数据驱动的过滤技术来构建1500多种语言的清洁,网挖数据集; (ii)通过利用大规模的多语言模型来开发用于服务不足的语言的实用MT模型,该模型训练了有监督的并行数据,以使用100多种高资源语言和单语言数据集,以增加1000多种语言; (iii)研究这些语言的评估指标的局限性,并对我们MT模型的输出进行定性分析,突出显示了这些类型模型的几种频繁误差模式。我们希望我们的工作为旨在为当前研究的语言构建MT系统的从业者提供有用的见解,并突出显示可以补充Data-Sparse设置中大量多语言模型的弱点的研究方向。
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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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我们想要模型的文本单位是什么?从字节到多字表达式,可以在许多粒度下分析和生成文本。直到最近,大多数自然语言处理(NLP)模型通过单词操作,将那些作为离散和原子令牌处理,但从字节对编码(BPE)开始,基于次字的方法在许多领域都变得占主导地位,使得仍然存在小词汇表允许快速推断。是道路字符级模型的结束或字节级处理吗?在这项调查中,我们通过展示和评估基于学习分割的词语和字符以及基于子字的方法的混合方法以及基于学习的分割的杂交方法,连接多行工作。我们得出结论,对于所有应用来说,并且可能永远不会成为所有应用的银子弹奇异解决方案,并且严重思考令牌化对许多应用仍然很重要。
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This paper presents the OPUS ecosystem with a focus on the development of open machine translation models and tools, and their integration into end-user applications, development platforms and professional workflows. We discuss our on-going mission of increasing language coverage and translation quality, and also describe on-going work on the development of modular translation models and speed-optimized compact solutions for real-time translation on regular desktops and small devices.
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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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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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State-of-the-art natural language processing systems rely on supervision in the form of annotated data to learn competent models. These models are generally trained on data in a single language (usually English), and cannot be directly used beyond that language. Since collecting data in every language is not realistic, there has been a growing interest in crosslingual language understanding (XLU) and low-resource cross-language transfer. In this work, we construct an evaluation set for XLU by extending the development and test sets of the Multi-Genre Natural Language Inference Corpus (MultiNLI) to 15 languages, including low-resource languages such as Swahili and Urdu. We hope that our dataset, dubbed XNLI, will catalyze research in cross-lingual sentence understanding by providing an informative standard evaluation task. In addition, we provide several baselines for multilingual sentence understanding, including two based on machine translation systems, and two that use parallel data to train aligned multilingual bag-of-words and LSTM encoders. We find that XNLI represents a practical and challenging evaluation suite, and that directly translating the test data yields the best performance among available baselines.
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一些基于变压器的模型可以执行跨语言转移学习:这些模型可以通过一种语言对特定任务进行培训,并以另一种语言的同一任务给予相对良好的结果,尽管仅在单语任务中进行了预先培训。但是,关于这些基于变压器的模型是否学习跨语言的通用模式,目前尚无共识。我们提出了一种单词级的任务不可能的方法,以评估此类模型构建的上下文化表示的对齐方式。我们表明,与以前的方法相比,我们的方法提供了更准确的翻译成对,以评估单词级别对齐。我们的结果表明,基于多语言变压器模型的某些内部层优于其他明确对齐的表示,甚至根据多语言对齐的更严格的定义,更是如此。
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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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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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In this work, we introduce IndicXTREME, a benchmark consisting of nine diverse tasks covering 18 languages from the Indic sub-continent belonging to four different families. Across languages and tasks, IndicXTREME contains a total of 103 evaluation sets, of which 51 are new contributions to the literature. To maintain high quality, we only use human annotators to curate or translate\footnote{for IndicXParaphrase, where an automatic translation system is used, a second human verification and correction step is done.} our datasets. To the best of our knowledge, this is the first effort toward creating a standard benchmark for Indic languages that aims to test the zero-shot capabilities of pretrained language models. We also release IndicCorp v2, an updated and much larger version of IndicCorp that contains 20.9 billion tokens in 24 languages. We pretrain IndicBERT v2 on IndicCorp v2 and evaluate it on IndicXTREME to show that it outperforms existing multilingual language models such as XLM-R and MuRIL.
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