机器翻译系统(MTS)是通过将文本或语音从一种语言转换为另一种语言的有效工具。在像印度这样的大型多语言环境中,对有效的翻译系统的需求变得显而易见,英语和一套印度语言(ILS)正式使用。与英语相反,由于语料库的不可用,IL仍然被视为低资源语言。为了解决不对称性质,多语言神经机器翻译(MNMT)系统会发展为在这个方向上的理想方法。在本文中,我们提出了一个MNMT系统,以解决与低资源语言翻译有关的问题。我们的模型包括两个MNMT系统,即用于英语印度(一对多),另一个用于指示英语(多一对多),其中包含15个语言对(30个翻译说明)的共享编码器码头。由于大多数IL对具有很少的平行语料库,因此不足以训练任何机器翻译模型。我们探索各种增强策略,以通过建议的模型提高整体翻译质量。最先进的变压器体系结构用于实现所提出的模型。大量数据的试验揭示了其优越性比常规模型的优势。此外,本文解决了语言关系的使用(在方言,脚本等方面),尤其是关于同一家族的高资源语言在提高低资源语言表现方面的作用。此外,实验结果还表明了ILS的倒退和域适应性的优势,以提高源和目标语言的翻译质量。使用所有这些关键方法,我们提出的模型在评估指标方面比基线模型更有效,即一组ILS的BLEU(双语评估研究)得分。
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Machine Translation (MT) system generally aims at automatic representation of source language into target language retaining the originality of context using various Natural Language Processing (NLP) techniques. Among various NLP methods, Statistical Machine Translation(SMT). SMT uses probabilistic and statistical techniques to analyze information and conversion. This paper canvasses about the development of bilingual SMT models for translating English to fifteen low-resource Indian Languages (ILs) and vice versa. At the outset, all 15 languages are briefed with a short description related to our experimental need. Further, a detailed analysis of Samanantar and OPUS dataset for model building, along with standard benchmark dataset (Flores-200) for fine-tuning and testing, is done as a part of our experiment. Different preprocessing approaches are proposed in this paper to handle the noise of the dataset. To create the system, MOSES open-source SMT toolkit is explored. Distance reordering is utilized with the aim to understand the rules of grammar and context-dependent adjustments through a phrase reordering categorization framework. In our experiment, the quality of the translation is evaluated using standard metrics such as BLEU, METEOR, and RIBES
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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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This paper demonstrates that multilingual denoising pre-training produces significant performance gains across a wide variety of machine translation (MT) tasks. We present mBART -a sequence-to-sequence denoising auto-encoder pre-trained on large-scale monolingual corpora in many languages using the BART objective . mBART is the first method for pre-training a complete sequence-to-sequence model by denoising full texts in multiple languages, while previous approaches have focused only on the encoder, decoder, or reconstructing parts of the text. Pre-training a complete model allows it to be directly fine tuned for supervised (both sentence-level and document-level) and unsupervised machine translation, with no task-specific modifications. We demonstrate that adding mBART initialization produces performance gains in all but the highest-resource settings, including up to 12 BLEU points for low resource MT and over 5 BLEU points for many document-level and unsupervised models. We also show it also enables new types of transfer to language pairs with no bi-text or that were not in the pre-training corpus, and present extensive analysis of which factors contribute the most to effective pre-training.
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只有在模型在大规模的多语言环境中培训的情况下,才有可能在无监督的机器翻译(UMT)上进行无监督的机器翻译(UMT),这意味着有能力的无监督翻译(例如尼泊尔或辛哈拉)的胜任的不受监督的翻译,例如尼泊尔或辛哈拉语。与高资源对应物混合。尽管如此,尽管高资源语言极大地帮助启动了目标低资源翻译任务,但它们之间的语言差异可能会阻碍他们的进一步改进。在这项工作中,我们提出了一个简单的完善程序,以将语言与预先训练的多语言UMT模型相关联,以仅关注目标低资源任务。我们的方法在完全无监督的翻译任务中实现了最新的尼泊尔,僧伽罗,古吉拉特语,拉脱维亚,爱沙尼亚和哈萨克的最新技术,分别为3.5、3.3、3.3、4.1、4.2、4.2和3.3。我们的代码库可从https://github.com/nxphi47/refine_unsup_multlingual_mt获得
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The machine translation mechanism translates texts automatically between different natural languages, and Neural Machine Translation (NMT) has gained attention for its rational context analysis and fluent translation accuracy. However, processing low-resource languages that lack relevant training attributes like supervised data is a current challenge for Natural Language Processing (NLP). We incorporated a technique known Active Learning with the NMT toolkit Joey NMT to reach sufficient accuracy and robust predictions of low-resource language translation. With active learning, a semi-supervised machine learning strategy, the training algorithm determines which unlabeled data would be the most beneficial for obtaining labels using selected query techniques. We implemented two model-driven acquisition functions for selecting the samples to be validated. This work uses transformer-based NMT systems; baseline model (BM), fully trained model (FTM) , active learning least confidence based model (ALLCM), and active learning margin sampling based model (ALMSM) when translating English to Hindi. The Bilingual Evaluation Understudy (BLEU) metric has been used to evaluate system results. The BLEU scores of BM, FTM, ALLCM and ALMSM systems are 16.26, 22.56 , 24.54, and 24.20, respectively. The findings in this paper demonstrate that active learning techniques helps the model to converge early and improve the overall quality of the translation system.
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在所有人类语言对之间实现通用翻译是机器翻译的圣杯(MT)研究。虽然最近在大量的多语言MT中的进展是达到这一目标的一步,但它变得明显,即简单地通过在更加平行数据上训练扩展多语言MT系统是不可编译的,因为用于低资源和非英语的标记数据的可用性 - 姓氏对禁止有限。为此,我们展示了一种务实的方法,可以使用监督和自我监督目标的混合来构建涵盖数百种语言的多语种MT模型,具体取决于不同语言对的数据可用性。我们展示这两种训练范例之间的协同作用使模型能够在零资源设置中产生高质量的翻译,甚至超过监控的用于中资和中资和中资质。我们开展广泛的实验,了解多语言监督,域错配和平行和单机数据量的效果,以了解我们自我监督的多语言模型的质量。为了展示方法的可扩展性,我们培训具有200多种语言的模型,并在几个先前研究的语言上展示了对零资源翻译的高性能。我们希望我们的调查结果将成为踏脚石,以便为下一千种语言进行翻译。
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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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语言模型预训练的最新进展利用大规模数据集创建多语言模型。但是,这些数据集中大多遗漏了低资源语言。这主要是因为网络上没有很好地表示口语,因此被排除在用于创建数据集的大规模爬网中。此外,这些模型的下游用户仅限于最初选择用于预训练的语言的选择。这项工作调查了如何最佳利用现有的预培训模型来为16种非洲语言创建低资源翻译系统。我们关注两个问题:1)如何将预训练的模型用于初始预培训中未包含的语言? 2)生成的翻译模型如何有效地转移到新域?为了回答这些问题,我们创建了一个新的非洲新闻语料库,涵盖16种语言,其中8种语言不属于任何现有评估数据集的一部分。我们证明,将两种语言转移到其他语言和其他领域的最有效策略是,以少量的高质量翻译数据微调大型预训练模型。
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我们对真正低资源语言的神经机翻译(NMT)进行了实证研究,并提出了一个训练课程,适用于缺乏并行培训数据和计算资源的情况,反映了世界上大多数世界语言和研究人员的现实致力于这些语言。以前,已经向低资源语言储存了使用后翻译(BT)和自动编码(AE)任务的无监督NMT。我们证明利用可比的数据和代码切换作为弱监管,与BT和AE目标相结合,即使仅使用适度的计算资源,低资源语言也会显着改进。在这项工作中提出的培训课程实现了Bleu分数,可通过+12.2 Bleu为古吉拉特和+3.7 Bleu为哈萨克斯培训的监督NMT培训,展示了弱势监督的巨大监督态度资源语言。在受到监督数据的培训时,我们的培训课程达到了索马里数据集(索马里29.3的BLEU的最先进的结果)。我们还观察到增加更多时间和GPU来培训可以进一步提高性能,强调报告在MT研究中的报告资源使用的重要性。
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我们提出了一种两阶段的培训方法,用于开发单个NMT模型,以翻译英语和英语的看不见的语言。对于第一阶段,我们将编码器模型初始化以鉴定XLM-R和Roberta的权重,然后对25种语言的平行数据进行多种语言微调。我们发现该模型可以推广到对看不见的语言的零击翻译。在第二阶段,我们利用这种概括能力从单语数据集生成合成的并行数据,然后用连续的反向翻译训练。最终模型扩展到了英语到许多方向,同时保持了多到英语的性能。我们称我们的方法为ecxtra(以英语为中心的跨语言(x)转移)。我们的方法依次利用辅助并行数据和单语言数据,并且在概念上很简单,仅在两个阶段都使用标准的跨熵目标。最终的ECXTRA模型对8种低资源语言的无监督NMT进行了评估,该语言为英语至哈萨克语(22.3> 10.4 bleu)以及其他15个翻译方向的竞争性能而获得了新的最先进。
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随着自然语言处理领域的最新发展,在使用不同架构的神经机翻译中的使用情况上升了。变压器架构用于实现最先进的准确性,但它们是训练的非常昂贵的昂贵。每个人都不能拥有由高端GPU和其他资源组成的等待。我们在低计算资源上培训我们的模型,并调查结果。正如预期的那样,变形金刚表现出其他架构,但结果有一些令人惊讶的结果。由更多编码器和解码器组成的变形金刚需要花更多的时间来训练,但有更少的BLEU分数。LSTM在实验中表现良好,比较少花时间训练而不是变压器,适合在具有时间限制的情况下使用。
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在任何翻译工作流程中,从源到目标的域知识保存至关重要。在翻译行业中,接收高度专业化的项目是很常见的,那里几乎没有任何平行的内域数据。在这种情况下,没有足够的内域数据来微调机器翻译(MT)模型,生成与相关上下文一致的翻译很具有挑战性。在这项工作中,我们提出了一种新颖的方法,用于域适应性,以利用最新的审计语言模型(LMS)来用于特定于域的MT的域数据增强,并模拟(a)的(a)小型双语数据集的域特征,或(b)要翻译的单语源文本。将这个想法与反翻译相结合,我们可以为两种用例生成大量的合成双语内域数据。为了进行调查,我们使用最先进的变压器体系结构。我们采用混合的微调来训练模型,从而显着改善了内域文本的翻译。更具体地说,在这两种情况下,我们提出的方法分别在阿拉伯语到英语对阿拉伯语言对上分别提高了大约5-6个BLEU和2-3 BLEU。此外,人类评估的结果证实了自动评估结果。
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本报告介绍了在大型多语种计算机翻译中为WMT21共享任务的Microsoft的机器翻译系统。我们参加了所有三种评估轨道,包括大轨道和两个小轨道,前者是无约束的,后两者完全受约束。我们的模型提交到共享任务的初始化用deltalm \脚注{\ url {https://aka.ms/deltalm}},一个通用的预训练的多语言编码器 - 解码器模型,并相应地使用巨大的收集并行进行微调数据和允许的数据源根据轨道设置,以及应用逐步学习和迭代背翻译方法进一步提高性能。我们的最终提交在自动评估度量方面排名第一的三条轨道。
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We propose a simple solution to use a single Neural Machine Translation (NMT) model to translate between multiple languages. Our solution requires no changes to the model architecture from a standard NMT system but instead introduces an artificial token at the beginning of the input sentence to specify the required target language. The rest of the model, which includes an encoder, decoder and attention module, remains unchanged and is shared across all languages. Using a shared wordpiece vocabulary, our approach enables Multilingual NMT using a single model without any increase in parameters, which is significantly simpler than previous proposals for Multilingual NMT. On the WMT'14 benchmarks, a single multilingual model achieves comparable performance for English→French and surpasses state-of-the-art results for English→German. Similarly, a single multilingual model surpasses state-of-the-art results for French→English and German→English on WMT'14 and WMT'15 benchmarks, respectively. On production corpora, multilingual models of up to twelve language pairs allow for better translation of many individual pairs. In addition to improving the translation quality of language pairs that the model was trained with, our models can also learn to perform implicit bridging between language pairs never seen explicitly during training, showing that transfer learning and zero-shot translation is possible for neural translation. Finally, we show analyses that hints at a universal interlingua representation in our models and show some interesting examples when mixing languages.
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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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我们介绍Samanantar,是最大的公开可用的并行Corpora Collection,用于指示语言。该集合中的英语和11个上线语言之间总共包含4970万句对(来自两种语言系列)。具体而言,我们从现有的公共可用并行基层编译1240万句对,另外,从网络上挖掘3740万句对,导致4倍增加。我们通过组合许多语料库,工具和方法来挖掘网站的并行句子:(a)Web爬行单格式语料库,(b)文档OCR,用于从扫描的文档中提取句子,(c)用于对齐句子的多语言表示模型,以及(d)近似最近的邻居搜索搜索大量句子。人类评估新矿业的Corpora的样本验证了11种语言的高质量平行句子。此外,我们使用英语作为枢轴语言,从英式并行语料库中提取所有55个指示语言对之间的834百万句子对。我们培训了跨越Samanantar上所有这些语言的多语种NMT模型,这在公开可用的基准上表现出现有的模型和基准,例如弗洛雷斯,建立萨曼塔尔的效用。我们的数据和模型可在Https://indicnlp.ai4bharat.org/samanantar/上公开提供,我们希望他们能够帮助推进NMT和Multibingual NLP的研究。
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翻译质量估计(QE)是预测机器翻译(MT)输出质量的任务,而无需任何参考。作为MT实际应用中的重要组成部分,这项任务已越来越受到关注。在本文中,我们首先提出了XLMRScore,这是一种基于使用XLM-Roberta(XLMR)模型计算的BertScore的简单无监督的QE方法,同时讨论了使用此方法发生的问题。接下来,我们建议两种减轻问题的方法:用未知令牌和预训练模型的跨语性对准替换未翻译的单词,以表示彼此之间的一致性单词。我们在WMT21 QE共享任务的四个低资源语言对上评估了所提出的方法,以及本文介绍的新的英语FARSI测试数据集。实验表明,我们的方法可以在两个零射击方案的监督基线中获得可比的结果,即皮尔森相关性的差异少于0.01,同时在所有低资源语言对中的平均低资源语言对中的无人看管竞争对手的平均水平超过8%的平均水平超过8%。 。
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最近在单语数据和机器翻译(MT)进行微调的预培训方面取得了成功,但尚不清楚如何最好地利用预先训练的模型来完成给定的MT任务。本文在微调MT上的预训练模型时研究了冻结参数的好处和缺点。我们专注于1)微调仅在英语单语言数据的BART上训练的模型。2)微调一个模型,该模型对25种语言的单语言数据进行了培训,Mbart。对于Bart,我们通过冻结大多数模型参数并添加额外的位置嵌入来获得最佳性能。对于MBART,我们将大多数语言对的天真微调的性能与编码器以及大多数解码器搭配。编码器的注意参数对于微调最重要。当将自己限制为越南人对英语的室外训练套装时,我们看到了基线的最大进步。
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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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