随着语言技术变得更加无处不在,越来越努力扩大自然语言处理(NLP)系统的语言分集和覆盖范围。可以说,影响现代NLP系统质量的最重要因素是数据可用性。在这项工作中,我们研究了NLP数据集的地理代表性,旨在量化NLP数据集与语言扬声器的预期需求量化。在这样做时,我们使用实体识别和链接系统,同时对其交叉量度的一致性进行重要观察,并为更强大的评估提供建议。最后,我们探讨了可能解释观察到的数据集发行版的一些地理和经济因素。此处提供代码和数据:https://github.com/ffaisal93/dataset_geography。此处提供其他可视化:https://nlp.cs.gmu.edu/project/datasetmaps/。
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We present, Naamapadam, the largest publicly available Named Entity Recognition (NER) dataset for the 11 major Indian languages from two language families. In each language, it contains more than 400k sentences annotated with a total of at least 100k entities from three standard entity categories (Person, Location and Organization) for 9 out of the 11 languages. The training dataset has been automatically created from the Samanantar parallel corpus by projecting automatically tagged entities from an English sentence to the corresponding Indian language sentence. We also create manually annotated testsets for 8 languages containing approximately 1000 sentences per language. We demonstrate the utility of the obtained dataset on existing testsets and the Naamapadam-test data for 8 Indic languages. We also release IndicNER, a multilingual mBERT model fine-tuned on the Naamapadam training set. IndicNER achieves the best F1 on the Naamapadam-test set compared to an mBERT model fine-tuned on existing datasets. IndicNER achieves an F1 score of more than 80 for 7 out of 11 Indic languages. The dataset and models are available under open-source licenses at https://ai4bharat.iitm.ac.in/naamapadam.
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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.
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Much recent progress in applications of machine learning models to NLP has been driven by benchmarks that evaluate models across a wide variety of tasks. However, these broad-coverage benchmarks have been mostly limited to English, and despite an increasing interest in multilingual models, a benchmark that enables the comprehensive evaluation of such methods on a diverse range of languages and tasks is still missing. To this end, we introduce the Cross-lingual TRansfer Evaluation of Multilingual Encoders (XTREME) benchmark, a multi-task benchmark for evaluating the cross-lingual generalization capabilities of multilingual representations across 40 languages and 9 tasks. We demonstrate that while models tested on English reach human performance on many tasks, there is still a sizable gap in the performance of cross-lingually transferred models, particularly on syntactic and sentence retrieval tasks. There is also a wide spread of results across languages. We release the benchmark 1 to encourage research on cross-lingual learning methods that transfer linguistic knowledge across a diverse and representative set of languages and tasks.
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知识库,例如Wikidata Amass大量命名实体信息,例如多语言标签,这些信息对于各种多语言和跨语义应用程序非常有用。但是,从信息一致性的角度来看,不能保证这样的标签可以跨语言匹配,从而极大地损害了它们对机器翻译等字段的有用性。在这项工作中,我们研究了单词和句子对准技术的应用,再加上匹配算法,以将从Wikidata提取的10种语言中提取的跨语性实体标签对齐。我们的结果表明,Wikidata的主标签之间的映射将通过任何使用的方法都大大提高(F1分数最高20美元)。我们展示了依赖句子嵌入的方法如何超过所有其他脚本,甚至在不同的脚本上。我们认为,这种技术在测量标签对的相似性上的应用,再加上富含高质量实体标签的知识库,是机器翻译的绝佳资产。
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我们介绍了关于多语言信息访问(MIA)2022共享任务的研讨会的结果,评估了16种类型上多样性的语言中的跨语性开放回程答案(QA)系统。在此任务中,我们在14种类型上多样化的语言中调整了两个大规模的跨语性开放式质疑QA数据集,并使用了2种代表性不足的语言中的新注释的开放式QA数据:Tagalog和Tamil。四个团队提交了他们的系统。利用迭代开采的最佳系统是不同的负面示例和较大的预审慎模型达到32.2 F1,表现优于我们的基线4.5分。第二最佳系统使用实体感知的上下文化表示文档检索,并在泰米尔语(20.8 F1)方面取得了重大改进,而其他大多数系统的得分几乎为零。
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While the NLP community is generally aware of resource disparities among languages, we lack research that quantifies the extent and types of such disparity. Prior surveys estimating the availability of resources based on the number of datasets can be misleading as dataset quality varies: many datasets are automatically induced or translated from English data. To provide a more comprehensive picture of language resources, we examine the characteristics of 156 publicly available NLP datasets. We manually annotate how they are created, including input text and label sources and tools used to build them, and what they study, tasks they address and motivations for their creation. After quantifying the qualitative NLP resource gap across languages, we discuss how to improve data collection in low-resource languages. We survey language-proficient NLP researchers and crowd workers per language, finding that their estimated availability correlates with dataset availability. Through crowdsourcing experiments, we identify strategies for collecting high-quality multilingual data on the Mechanical Turk platform. We conclude by making macro and micro-level suggestions to the NLP community and individual researchers for future multilingual data development.
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我们介绍了Paranames,这是一种多语言并行名称资源,由1.18亿个名称组成,涉及400种语言。为1360万个实体提供了名称,这些实体映射到标准化实体类型(每/loc/org)。使用Wikidata作为来源,我们创建了此类类型的最大资源。我们描述了我们过滤和标准化数据以提供最佳质量的方法。PANAMES对于多语言语言处理非常有用,既可以定义名称翻译/音译的任务,又可以作为任务的补充数据,例如命名实体识别和链接。我们通过训练与英文和英语的规范名称翻译的多语言模型来展示对照群的应用。我们的资源是根据https://github.com/bltlab/paranames发布的创意共享许可证(CC By 4.0)发布的。
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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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我们提出了多语言数据集的Multiconer,用于命名实体识别,涵盖11种语言的3个域(Wiki句子,问题和搜索查询),以及多语言和代码混合子集。该数据集旨在代表NER中的当代挑战,包括低文字方案(简短和未添加的文本),句法复杂的实体(例如电影标题)和长尾实体分布。使用基于启发式的句子采样,模板提取和插槽以及机器翻译等技术,从公共资源中汇编了26M令牌数据集。我们在数据集上应用了两个NER模型:一个基线XLM-Roberta模型和一个最先进的Gemnet模型,该模型利用了Gazetteers。基线实现了中等的性能(Macro-F1 = 54%),突出了我们数据的难度。 Gemnet使用Gazetteers,显着改善(Macro-F1 =+30%的平均改善)。甚至对于大型预训练的语言模型,多功能人也会构成挑战,我们认为它可以帮助进一步研究建立强大的NER系统。 Multiconer可在https://registry.opendata.aws/multiconer/上公开获取,我们希望该资源将有助于推进NER各个方面的研究。
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意大利的特征是欧洲一种一种独一无二的语言多样性格局,该景观暗中编码了当地知识,文化传统,艺术表达及其演讲者的历史。但是,意大利的30多种语言品种有几代人内消失的风险。语言技术在保存濒危语言方面具有主要作用,但是目前,它在资源不足,主要缺乏标准拼写术的品种中挣扎,主要用于口语环境。在本文中,我们介绍了意大利的语言背景,并讨论了意大利语言品种开发NLP技术面临的挑战。我们提供潜在的方向,并倡导从以机器为中心转向以说话者为中心的NLP的范式转变。最后,我们建议建立一个当地社区,旨在为意大利语言和方言的言语和语言技术负责,参与式发展。
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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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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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Open-Domain Generative Question Answering has achieved impressive performance in English by combining document-level retrieval with answer generation. These approaches, which we refer to as GenQA, can generate complete sentences, effectively answering both factoid and non-factoid questions. In this paper, we extend GenQA to the multilingual and cross-lingual settings. For this purpose, we first introduce GenTyDiQA, an extension of the TyDiQA dataset with well-formed and complete answers for Arabic, Bengali, English, Japanese, and Russian. Based on GenTyDiQA, we design a cross-lingual generative model that produces full-sentence answers by exploiting passages written in multiple languages, including languages different from the question. Our cross-lingual generative system outperforms answer sentence selection baselines for all 5 languages and monolingual generative pipelines for three out of five languages studied.
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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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本文介绍了对土耳其语可用于的语料库和词汇资源的全面调查。我们审查了广泛的资源,重点关注公开可用的资源。除了提供有关可用语言资源的信息外,我们还提供了一组建议,并确定可用于在土耳其语言学和自然语言处理中进行研究和建筑应用的数据中的差距。
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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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GPT-3等大型自回归语言模型是几秒钟的学习者,可以在没有微调的情况下执行各种语言任务。虽然已知这些模型能够共同代表许多不同的语言,但他们的培训数据由英语主导,可能限制了它们的交叉概括。在这项工作中,我们在覆盖多种语言的平衡语料库上培训多语言自回归语言模型,并在广泛的任务中研究他们几乎没有零点的学习能力。我们最大的模型,具有75亿参数,在20多种代表语言中,在几种代表语言中,在几种代表性语言中,在几种代表性语言中,在多语言型号推理中表现出可比大小的GPT-3(在0次设置和0次拍摄设置中的绝对精度改善+ 7.4% 4-拍摄设置中的9.4%)和自然语言推理(每次拍摄和4次设置中的每一个+ 5.4%)。在Flores-101机器翻译基准测试中,我们的模型优于GPT-3在182个翻译方向上有32个培训例子,同时超过45个方向的官方监督基线。我们介绍了模型成功和失败的位置的详细分析,特别是它尤其显示在某些任务中实现交叉语境的内容学习,而仍然存在改善表面的鲁棒性和适应没有a的任务的余地自然冻结形式。最后,我们评估我们在仇恨语音检测中以五种语言的仇恨语音检测的模型,并发现它具有与可比大小的GPT-3模型类似的限制。
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Task-oriented dialogue (TOD) systems have been applied in a range of domains to support human users to achieve specific goals. Systems are typically constructed for a single domain or language and do not generalise well beyond this. Their extension to other languages in particular is restricted by the lack of available training data for many of the world's languages. To support work on Natural Language Understanding (NLU) in TOD across multiple languages and domains simultaneously, we constructed MULTI3NLU++, a multilingual, multi-intent, multi-domain dataset. MULTI3NLU++ extends the English-only NLU++ dataset to include manual translations into a range of high, medium and low resource languages (Spanish, Marathi, Turkish and Amharic), in two domains (banking and hotels). MULTI3NLU++ inherits the multi-intent property of NLU++, where an utterance may be labelled with multiple intents, providing a more realistic representation of a user's goals and aligning with the more complex tasks that commercial systems aim to model. We use MULTI3NLU++ to benchmark state-of-the-art multilingual language models as well as Machine Translation and Question Answering systems for the NLU task of intent detection for TOD systems in the multilingual setting. The results demonstrate the challenging nature of the dataset, particularly in the low-resource language setting.
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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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