Machine Translation Quality Estimation (QE) is the task of evaluating translation output in the absence of human-written references. Due to the scarcity of human-labeled QE data, previous works attempted to utilize the abundant unlabeled parallel corpora to produce additional training data with pseudo labels. In this paper, we demonstrate a significant gap between parallel data and real QE data: for QE data, it is strictly guaranteed that the source side is original texts and the target side is translated (namely translationese). However, for parallel data, it is indiscriminate and the translationese may occur on either source or target side. We compare the impact of parallel data with different translation directions in QE data augmentation, and find that using the source-original part of parallel corpus consistently outperforms its target-original counterpart. Moreover, since the WMT corpus lacks direction information for each parallel sentence, we train a classifier to distinguish source- and target-original bitext, and carry out an analysis of their difference in both style and domain. Together, these findings suggest using source-original parallel data for QE data augmentation, which brings a relative improvement of up to 4.0% and 6.4% compared to undifferentiated data on sentence- and word-level QE tasks respectively.
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我们描述了JD Explore Academy对WMT 2022共享的一般翻译任务的提交。我们参加了所有高资源曲目和一条中型曲目,包括中文英语,德语英语,捷克语英语,俄语 - 英语和日语英语。我们通过扩大两个主要因素,即语言对和模型大小,即\ textbf {vega-mt}系统来推动以前的工作的极限 - 进行翻译的双向培训。至于语言对,我们将“双向”扩展到“多向”设置,涵盖所有参与语言,以利用跨语言的常识,并将其转移到下游双语任务中。至于型号尺寸,我们将变压器限制到拥有近47亿参数的极大模型,以完全增强我们VEGA-MT的模型容量。此外,我们采用数据增强策略,例如单语数据的循环翻译以及双语和单语数据的双向自我训练,以全面利用双语和单语言数据。为了使我们的Vega-MT适应通用域测试集,设计了概括调整。根据受约束系统的官方自动分数,根据图1所示的sacrebleu,我们在{zh-en(33.5),en-zh(49.7)(49.7),de-en(33.7)上获得了第一名-de(37.8),CS-EN(54.9),En-CS(41.4)和En-Ru(32.7)},在{ru-en(45.1)和Ja-en(25.6)}和第三名上的第二名和第三名在{en-ja(41.5)}上; W.R.T彗星,我们在{zh-en(45.1),en-zh(61.7),de-en(58.0),en-de(63.2),cs-en(74.7),ru-en(ru-en(ru-en)上,我们获得了第一名64.9),en-ru(69.6)和en-ja(65.1)},分别在{en-cs(95.3)和ja-en(40.6)}上的第二名。将发布模型,以通过GitHub和Omniforce平台来促进MT社区。
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With the recent advance in neural machine translation demonstrating its importance, research on quality estimation (QE) has been steadily progressing. QE aims to automatically predict the quality of machine translation (MT) output without reference sentences. Despite its high utility in the real world, there remain several limitations concerning manual QE data creation: inevitably incurred non-trivial costs due to the need for translation experts, and issues with data scaling and language expansion. To tackle these limitations, we present QUAK, a Korean-English synthetic QE dataset generated in a fully automatic manner. This consists of three sub-QUAK datasets QUAK-M, QUAK-P, and QUAK-H, produced through three strategies that are relatively free from language constraints. Since each strategy requires no human effort, which facilitates scalability, we scale our data up to 1.58M for QUAK-P, H and 6.58M for QUAK-M. As an experiment, we quantitatively analyze word-level QE results in various ways while performing statistical analysis. Moreover, we show that datasets scaled in an efficient way also contribute to performance improvements by observing meaningful performance gains in QUAK-M, P when adding data up to 1.58M.
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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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Given a document in a source language, cross-lingual summarization (CLS) aims at generating a concise summary in a different target language. Unlike monolingual summarization (MS), naturally occurring source-language documents paired with target-language summaries are rare. To collect large-scale CLS samples, existing datasets typically involve translation in their creation. However, the translated text is distinguished from the text originally written in that language, i.e., translationese. Though many efforts have been devoted to CLS, none of them notice the phenomenon of translationese. In this paper, we first confirm that the different approaches to constructing CLS datasets will lead to different degrees of translationese. Then we design systematic experiments to investigate how translationese affects CLS model evaluation and performance when it appears in source documents or target summaries. In detail, we find that (1) the translationese in documents or summaries of test sets might lead to the discrepancy between human judgment and automatic evaluation; (2) the translationese in training sets would harm model performance in the real scene; (3) though machine-translated documents involve translationese, they are very useful for building CLS systems on low-resource languages under specific training strategies. Furthermore, we give suggestions for future CLS research including dataset and model developments. We hope that our work could let researchers notice the phenomenon of translationese in CLS and take it into account in the future.
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机器翻译(MT)的单词级质量估计(QE)旨在在不参考的情况下找出翻译句子中的潜在翻译错误。通常,关于文字级别量化宽松的传统作品旨在根据文章编辑工作来预测翻译质量,其中通过比较MT句子之间的单词来自动生成单词标签(“ OK”和“ BAD”)。通过翻译错误率(TER)工具包编辑的句子。虽然可以使用后编辑的工作来在一定程度上测量翻译质量,但我们发现它通常与人类对单词是否良好或翻译不良的判断相抵触。为了克服限制,我们首先创建了一个金色基准数据集,即\ emph {hjqe}(人类对质量估计的判断),专家翻译直接注释了对其判断的不良翻译单词。此外,为了进一步利用平行语料库,我们提出了使用两个标签校正策略的自我监督的预训练,即标记改进策略和基于树的注释策略,以使基于TER的人工量化量子ceper更接近\ emph {HJQE}。我们根据公开可用的WMT en-de和en-ZH Corpora进行实质性实验。结果不仅表明我们提出的数据集与人类的判断更加一致,而且还确认了提议的标签纠正策略的有效性。 。}
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评估指标是文本生成系统的关键成分。近年来,已经提出了几十年前的文本生成质量的人类评估,提出了几个基于伯特的评估指标(包括Bertscore,Moverscore,BLEurt等),这些评估与文本生成质量的人类评估比Bleu或Rouge进行了更好。但是,很少是已知这些度量基于黑盒语言模型表示的指标实际捕获(通常假设它们模型语义相似性)。在这项工作中,我们使用基于简单的回归的全局解释技术来沿着语言因素解开度量标准分数,包括语义,语法,形态和词汇重叠。我们表明,不同的指标捕获了一定程度的各个方面,但它们对词汇重叠大大敏感,就像Bleu和Rouge一样。这暴露了这些新颖性拟议的指标的限制,我们还在对抗对抗测试场景中突出显示。
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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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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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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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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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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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我们定义了一个名为“扩展单词对齐”的新颖概念,以提高后编辑辅助效率。基于扩展的单词对齐方式,我们进一步提出了一个名为精制单词级量化宽松的新颖任务,该任务输出精制标签和单词级对应关系。与原始单词级别的量化宽松相比,新任务能够直接指出编辑操作,从而提高效率。为了提取扩展单词对齐,我们采用了基于Mbert的监督方法。为了解决精致的单词级量化宽松,我们首先通过训练基于Mbert和XLM-R的序列标记的回归模型来预测原始量化量子标签。然后,我们使用扩展单词对齐来完善原始文字标签。另外,我们提取源差距对应关系,同时获得GAP标签。两种语言对的实验显示了我们方法的可行性,并为我们提供了进一步改进的灵感。
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监督机器翻译的绝大多数评估指标,即(i)假设参考翻译的存在,(ii)受到人体得分的培训,或(iii)利用并行数据。这阻碍了其适用于此类监督信号的情况。在这项工作中,我们开发了完全无监督的评估指标。为此,我们利用评估指标,平行语料库开采和MT系统之间的相似性和协同作用。特别是,我们使用无监督的评估指标来开采伪并行数据,我们用来重塑缺陷的基础向量空间(以迭代方式),并诱导无监督的MT系统,然后提供伪引用作为伪参考作为在中的附加组件中的附加组件指标。最后,我们还从伪并行数据中诱导无监督的多语言句子嵌入。我们表明,我们完全无监督的指标是有效的,即,他们在5个评估数据集中的4个击败了受监督的竞争对手。
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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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人类翻译的文本以同一语言显示出与自然书面文本的不同特征。这种现象被称为翻译人员,被认为是将机器翻译(MT)评估混淆。但是,我们发现现有的翻译工作忽略了一些重要因素,结论主要是相关的,但不是因果关系。在这项工作中,我们收集了Causalmt,这是一个数据集,其中MT培训数据还标有人类翻译方向。我们检查了两个关键因素,即火车测试方向匹配(是否对齐训练和测试集中的人类翻译方向)和数据模型方向匹配(该模型是否沿与人类翻译方向相同的方向学习数据集)。我们表明,这两个因素对MT的性能具有很大的因果影响,除了测试模型方向不匹配的情况下,现有工作对TranslationEse的影响强调了。鉴于我们的发现,我们为MT培训和评估提供了一系列建议。我们的代码和数据在https://github.com/edisonni-hku/causalmt上
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自动编辑(APE)旨在通过自动纠正机器翻译输出中的错误来减少手动后编辑工作。由于人类注销的培训数据数量有限,数据稀缺是所有猿类系统所面临的主要挑战之一。为了减轻缺乏真正的培训数据,当前的大多数猿类系统采用数据增强方法来生成大规模的人工语料库。鉴于APE数据增强的重要性,我们分别研究了人工语料库的构建方法和人工数据域对猿类模型性能的影响。此外,猿类的难度在不同的机器翻译(MT)系统之间有所不同。我们在困难的猿数据集上研究了最先进的APE模型的输出,以分析现有的APE系统中的问题。首先,我们发现1)具有高质量源文本和机器翻译文本的人工语料库更有效地改善了猿类模型的性能; 2)内域人工训练数据可以更好地改善猿类模型的性能,而无关紧要的外域数据实际上会干扰该模型; 3)现有的APE模型与包含长源文本或高质量机器翻译文本的案例斗争; 4)最先进的猿类模型在语法和语义添加问题上很好地工作,但是输出容易出现实体和语义遗漏误差。
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对于多语言序列到序列预审预周序模型(多语言SEQ2SEQ PLM),例如姆巴特(Mbart),自制的预处理任务接受了多种单语言的培训,例如25种来自CommonCrawl的语言,而下游的跨语言任务通常在双语语言子集上进行,例如英语 - 德国人,存在数据差异,即领域的差异,以及跨语言学习客观差异,即在训练和填充阶段之间的任务差异。为了弥合上述跨语言域和任务差距,我们将使用额外的代码切换恢复任务扩展了香草预后管道。具体而言,第一阶段采用自我监督的代码转换还原任务作为借口任务,从而允许多语言SEQ2SEQ PLM获取一些域内对齐信息。在第二阶段,我们正常在下游数据上微调模型。 NLG评估(12个双语翻译任务,30个零射击任务和2项跨语言摘要任务)和NLU评估(7个跨语性自然语言推理任务)的实验表明,我们的模型超过了强大的基线MBART,具有标准的FINETUNNING,这表明了我们的模型策略,一致。分析表明,我们的方法可以缩小跨语性句子表示的欧几里得距离,并通过微不足道的计算成本改善模型概括。我们在:https://github.com/zanchangtong/csr4mbart上发布代码。
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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.
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本文介绍了一种新的数据增强方法,用于神经机器翻译,该方法可以在语言内部和跨语言内部实施更强的语义一致性。我们的方法基于条件掩盖语言模型(CMLM),该模型是双向的,可以在左右上下文以及标签上有条件。我们证明CMLM是生成上下文依赖性单词分布的好技术。特别是,我们表明CMLM能够通过在替换过程中对源和目标进行调节来实现语义一致性。此外,为了增强多样性,我们将软词替换的想法纳入了数据增强,该概念用词汇上的概率分布代替了一个单词。在不同量表的四个翻译数据集上进行的实验表明,总体解决方案会导致更现实的数据增强和更好的翻译质量。与最新作品相比,我们的方法始终取得了最佳性能,并且在基线上的提高了1.90个BLEU点。
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