Recent work attributes progress in NLP to large language models (LMs) with increased model size and large quantities of pretraining data. Despite this, current state-of-the-art LMs for Hebrew are both under-parameterized and under-trained compared to LMs in other languages. Additionally, previous work on pretrained Hebrew LMs focused on encoder-only models. While the encoder-only architecture is beneficial for classification tasks, it does not cater well for sub-word prediction tasks, such as Named Entity Recognition, when considering the morphologically rich nature of Hebrew. In this paper we argue that sequence-to-sequence generative architectures are more suitable for LLMs in the case of morphologically rich languages (MRLs) such as Hebrew. We demonstrate that by casting tasks in the Hebrew NLP pipeline as text-to-text tasks, we can leverage powerful multilingual, pretrained sequence-to-sequence models as mT5, eliminating the need for a specialized, morpheme-based, separately fine-tuned decoder. Using this approach, our experiments show substantial improvements over previously published results on existing Hebrew NLP benchmarks. These results suggest that multilingual sequence-to-sequence models present a promising building block for NLP for MRLs.
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预先训练的上下文化文本表示模型学习自然语言的有效表示,以使IT机器可以理解。在注意机制的突破之后,已经提出了新一代预磨模的模型,以便自变压器引入以来实现了良好的性能。来自变压器(BERT)的双向编码器表示已成为语言理解的最先进的模型。尽管取得了成功,但大多数可用的型号已经在印度欧洲语言中培训,但是对代表性的语言和方言的类似研究仍然稀疏。在本文中,我们调查了培训基于单语言变换器的语言模型的可行性,以获得代表语言的特定重点是突尼斯方言。我们评估了我们的语言模型对情感分析任务,方言识别任务和阅读理解问答任务。我们表明使用嘈杂的Web爬网数据而不是结构化数据(维基百科,文章等)更方便这些非标准化语言。此外,结果表明,相对小的Web爬网数据集导致与使用较大数据集获得的那些表现相同的性能。最后,我们在所有三个下游任务中达到或改善了最先进的Tunbert模型。我们释放出Tunbert净化模型和用于微调的数据集。
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Multilingual language models (MLMs) acquire valuable, generalizable linguistic information during pretraining and have advanced the state of the art on task-specific finetuning. So far, only ~ 28 out of ~2,000 African languages are covered in existing language models. We ameliorate this limitation by developing SERENGETI, a set of massively multilingual language model that covers 517 African languages and language varieties. We evaluate our novel models on eight natural language understanding tasks across 20 datasets, comparing to four MLMs that each cover any number of African languages. SERENGETI outperforms other models on 11 datasets across the eights tasks and achieves 82.27 average F-1. We also perform error analysis on our models' performance and show the influence of mutual intelligibility when the models are applied under zero-shot settings. We will publicly release our models for research.
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Given the impact of language models on the field of Natural Language Processing, a number of Spanish encoder-only masked language models (aka BERTs) have been trained and released. These models were developed either within large projects using very large private corpora or by means of smaller scale academic efforts leveraging freely available data. In this paper we present a comprehensive head-to-head comparison of language models for Spanish with the following results: (i) Previously ignored multilingual models from large companies fare better than monolingual models, substantially changing the evaluation landscape of language models in Spanish; (ii) Results across the monolingual models are not conclusive, with supposedly smaller and inferior models performing competitively. Based on these empirical results, we argue for the need of more research to understand the factors underlying them. In this sense, the effect of corpus size, quality and pre-training techniques need to be further investigated to be able to obtain Spanish monolingual models significantly better than the multilingual ones released by large private companies, specially in the face of rapid ongoing progress in the field. The recent activity in the development of language technology for Spanish is to be welcomed, but our results show that building language models remains an open, resource-heavy problem which requires to marry resources (monetary and/or computational) with the best research expertise and practice.
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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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大型审慎的语言模型最近征服了自然语言处理领域。作为BERT中引入的主要掩盖语言建模的替代方案,T5模型引入了更通用的训练目标,即序列转换的顺序,其中包括蒙版语言模型,但自然地适合文本生成任务,例如机器翻译,摘要,开放 - 开放 - 域问题回答,文本简化,对话系统等。T5模型的单语变体仅限于资源良好的语言,而大量的多语言T5模型则支持101种语言。相比之下,我们训练了两个不同尺寸的T5型序列,以使用较少的资源并分析其行为的形态丰富的斯洛文尼语的序列模型。关于分类任务,SLOT5模型主要落后于单语Slovene Sloberta模型,但应考虑生成任务。
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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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语言模型是通过有限的输入集定义的,当我们尝试扩展支持语言的数量时,该输入会产生词汇瓶颈。解决此瓶颈会导致在嵌入矩阵中可以表示的与输出层中的计算问题之间的权衡。本文介绍了基于像素的语言编码器Pixel,这两个问题都没有遭受这些问题的影响。 Pixel是一种验证的语言模型,可将文本作为图像呈现,使基于拼字法相似性或像素的共激活的语言传输表示形式。 Pixel经过训练可以重建蒙版贴片的像素,而不是预测令牌上的分布。我们在与BERT相同的英语数据上为8600万参数像素模型预告,并对包括各种非拉丁语脚本在内的类型上多样化的语言中的句法和语义任务进行了评估。我们发现,Pixel在预读取数据中找不到的脚本上的句法和语义处理任务大大优于BERT,但是在使用拉丁文脚本时,Pixel比BERT稍弱。此外,我们发现像素对嘈杂的文本输入比bert更强大,进一步证实了用像素建模语言的好处。
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通过共享数据集和基准,已经促进了语音处理的进展。历史上,这些都集中在自动语音识别(ASR),扬声器标识或其他较低级别的任务上。兴趣在更高层次的口语中越来越多,理解任务,包括使用端到端模型,但是此类任务的注释数据集较少。与此同时,最近的工作显示了预先培训通用表示的可能性,然后使用相对较少标记的数据进行微调的多个任务。我们建议为口语语言理解(屠宰)创建一套基准任务,由有限尺寸标记的培训集和相应的评估集组成。该资源将允许研究界跟踪进度,评估高级任务的预先接受预期的表示,并研究开放的问题,例如管道与端到端方法的实用性。我们介绍了雪橇基准套件的第一阶段,包括指定实体识别,情感分析和相应数据集上的ASR。我们专注于自然产生的(未读取或综合)语音和自由可用的数据集。我们为VoxceReb和Voxpopuli数据集的子集提供新的转录和注释,基线模型的评估指标和结果,以及重现基线的开源工具包,并评估新模型。
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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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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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Due to their crucial role in all NLP, several benchmarks have been proposed to evaluate pretrained language models. In spite of these efforts, no public benchmark of diverse nature currently exists for evaluation of Arabic. This makes it challenging to measure progress for both Arabic and multilingual language models. This challenge is compounded by the fact that any benchmark targeting Arabic needs to take into account the fact that Arabic is not a single language but rather a collection of languages and varieties. In this work, we introduce ORCA, a publicly available benchmark for Arabic language understanding evaluation. ORCA is carefully constructed to cover diverse Arabic varieties and a wide range of challenging Arabic understanding tasks exploiting 60 different datasets across seven NLU task clusters. To measure current progress in Arabic NLU, we use ORCA to offer a comprehensive comparison between 18 multilingual and Arabic language models. We also provide a public leaderboard with a unified single-number evaluation metric (ORCA score) to facilitate future research.
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通常,在自然语言处理领域,识别指定实体是一项实用且具有挑战性的任务。由于混合的性质导致语言复杂性,因此在代码混合文本上命名的实体识别是进一步的挑战。本文介绍了CMNERONE团队在Semeval 2022共享任务11 Multiconer的提交。代码混合的NER任务旨在识别代码混合数据集中的命名实体。我们的工作包括在代码混合数据集上的命名实体识别(NER),来利用多语言数据。我们的加权平均F1得分为0.7044,即比基线大6%。
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微调下游任务的预培训语言模型已成为NLP的标准。最近发现,基于高级推理任务的中间培训如问题应答(QA)可以提高某些语言模型的目标任务的性能。然而,如果中间培训通常有益于各种语言模型,则不清楚。本文使用Squad-2.0 QA任务进行目标文本分类任务的中间培训,我们在使用两个基础和两个紧凑的语言模型的六个任务中尝试了八个任务和六个任务。我们的实验表明,基于QA的中间培训在不同语言模型中产生不同的传输性能,除了类似的QA任务。
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BERT,ROBERTA或GPT-3等复杂的基于注意力的语言模型的外观已允许在许多场景中解决高度复杂的任务。但是,当应用于特定域时,这些模型会遇到相当大的困难。诸如Twitter之类的社交网络就是这种情况,Twitter是一种不断变化的信息流,以非正式和复杂的语言编写的信息流,鉴于人类的重要作用,每个信息都需要仔细评估,即使人类也需要理解。通过自然语言处理解决该领域的任务涉及严重的挑战。当将强大的最先进的多语言模型应用于这种情况下,特定语言的细微差别用来迷失翻译。为了面对这些挑战,我们提出了\ textbf {bertuit},这是迄今为止针对西班牙语提出的较大变压器,使用Roberta Optimization进行了230m西班牙推文的大规模数据集进行了预培训。我们的动机是提供一个强大的资源,以更好地了解西班牙Twitter,并用于专注于该社交网络的应用程序,特别强调致力于解决该平台中错误信息传播的解决方案。对Bertuit进行了多个任务评估,并与M-Bert,XLM-Roberta和XLM-T进行了比较,该任务非常具有竞争性的多语言变压器。在这种情况下,使用应用程序显示了我们方法的实用性:一种可视化骗局和分析作者群体传播虚假信息的零击方法。错误的信息在英语以外的其他语言等平台上疯狂地传播,这意味着在英语说话之外转移时,变形金刚的性能可能会受到影响。
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Transformer language models (TLMs) are critical for most NLP tasks, but they are difficult to create for low-resource languages because of how much pretraining data they require. In this work, we investigate two techniques for training monolingual TLMs in a low-resource setting: greatly reducing TLM size, and complementing the masked language modeling objective with two linguistically rich supervised tasks (part-of-speech tagging and dependency parsing). Results from 7 diverse languages indicate that our model, MicroBERT, is able to produce marked improvements in downstream task evaluations relative to a typical monolingual TLM pretraining approach. Specifically, we find that monolingual MicroBERT models achieve gains of up to 18% for parser LAS and 11% for NER F1 compared to a multilingual baseline, mBERT, while having less than 1% of its parameter count. We conclude reducing TLM parameter count and using labeled data for pretraining low-resource TLMs can yield large quality benefits and in some cases produce models that outperform multilingual approaches.
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Spoken language understanding (SLU) tasks have been studied for many decades in the speech research community, but have not received as much attention as lower-level tasks like speech and speaker recognition. In particular, there are not nearly as many SLU task benchmarks, and many of the existing ones use data that is not freely available to all researchers. Recent work has begun to introduce such benchmark datasets for several tasks. In this work, we introduce several new annotated SLU benchmark tasks based on freely available speech data, which complement existing benchmarks and address gaps in the SLU evaluation landscape. We contribute four tasks: question answering and summarization involve inference over longer speech sequences; named entity localization addresses the speech-specific task of locating the targeted content in the signal; dialog act classification identifies the function of a given speech utterance. We follow the blueprint of the Spoken Language Understanding Evaluation (SLUE) benchmark suite. In order to facilitate the development of SLU models that leverage the success of pre-trained speech representations, we will be publishing for each task (i) annotations for a relatively small fine-tuning set, (ii) annotated development and test sets, and (iii) baseline models for easy reproducibility and comparisons. In this work, we present the details of data collection and annotation and the performance of the baseline models. We also perform sensitivity analysis of pipeline models' performance (speech recognizer + text model) to the speech recognition accuracy, using more than 20 state-of-the-art speech recognition models.
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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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We present SpanBERT, a pre-training method that is designed to better represent and predict spans of text. Our approach extends BERT by (1) masking contiguous random spans, rather than random tokens, and (2) training the span boundary representations to predict the entire content of the masked span, without relying on the individual token representations within it. Span-BERT consistently outperforms BERT and our better-tuned baselines, with substantial gains on span selection tasks such as question answering and coreference resolution. In particular, with the same training data and model size as BERT large , our single model obtains 94.6% and 88.7% F1 on SQuAD 1.1 and 2.0 respectively. We also achieve a new state of the art on the OntoNotes coreference resolution task (79.6% F1), strong performance on the TACRED relation extraction benchmark, and even gains on GLUE. 1 * Equal contribution. 1 Our code and pre-trained models are available at https://github.com/facebookresearch/ SpanBERT.
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We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a;Radford et al., 2018), BERT is designed to pretrain deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be finetuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial taskspecific architecture modifications.BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).
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