Neural machine translation (NMT) has become the de-facto standard in real-world machine translation applications. However, NMT models can unpredictably produce severely pathological translations, known as hallucinations, that seriously undermine user trust. It becomes thus crucial to implement effective preventive strategies to guarantee their proper functioning. In this paper, we address the problem of hallucination detection in NMT by following a simple intuition: as hallucinations are detached from the source content, they exhibit encoder-decoder attention patterns that are statistically different from those of good quality translations. We frame this problem with an optimal transport formulation and propose a fully unsupervised, plug-in detector that can be used with any attention-based NMT model. Experimental results show that our detector not only outperforms all previous model-based detectors, but is also competitive with detectors that employ large models trained on millions of samples.

尽管神经机器翻译（NMT）中幻觉的问题受到了一些关注，但对这种高度病理现象的研究缺乏坚实的基础。以前的工作在几种方面受到限制：它通常诉诸于放大问题的人工环境，它无视一些（常见的）幻觉类型，并且不能验证检测启发式方法的充分性。在本文中，我们为研究NMT幻觉的研究设定了基础。首先，我们在自然环境中工作，即没有人造噪声的内域数据，既不在训练中也没有推理。接下来，我们注释一个超过3.4K句子的数据集，指示不同类型的关键错误和幻觉。然后，我们转向以前使用的检测方法和两种重新访问方法，并建议使用基于玻璃盒的不确定性检测器。总体而言，我们表明，对于预防性设置，（i）先前使用的方法在很大程度上不足，（ii）序列对数概要性效果最好，并且与基于参考的方法相同。最后，我们提出了脱足素剂，这是一种减轻测试时间的简单方法，可大大降低幻觉速度。为了简化未来的研究，我们发布了用于WMT18德语英语数据的注释数据集以及模型，培训数据和代码。

While the problem of hallucinations in neural machine translation has long been recognized, so far the progress on its alleviation is very little. Indeed, recently it turned out that without artificially encouraging models to hallucinate, previously existing methods fall short and even the standard sequence log-probability is more informative. It means that characteristics internal to the model can give much more information than we expect, and before using external models and measures, we first need to ask: how far can we go if we use nothing but the translation model itself ? We propose to use a method that evaluates the percentage of the source contribution to a generated translation. Intuitively, hallucinations are translations "detached" from the source, hence they can be identified by low source contribution. This method improves detection accuracy for the most severe hallucinations by a factor of 2 and is able to alleviate hallucinations at test time on par with the previous best approach that relies on external models. Next, if we move away from internal model characteristics and allow external tools, we show that using sentence similarity from cross-lingual embeddings further improves these results.

As more and more conversational and translation systems are deployed in production, it is essential to implement and to develop effective control mechanisms guaranteeing their proper functioning and security. An essential component to ensure safe system behavior is out-of-distribution (OOD) detection, which aims at detecting whether an input sample is statistically far from the training distribution. Although OOD detection is a widely covered topic in classification tasks, it has received much less attention in text generation. This paper addresses the problem of OOD detection for machine translation and dialog generation from an operational perspective. Our contributions include: (i) RAINPROOF a Relative informAItioN Projection ODD detection framework; and (ii) a more operational evaluation setting for OOD detection. Surprisingly, we find that OOD detection is not necessarily aligned with task-specific measures. The OOD detector may filter out samples that are well processed by the model and keep samples that are not, leading to weaker performance. Our results show that RAINPROOF breaks this curse and achieve good results in OOD detection while increasing performance.

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.

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.

我用Hunglish2语料库训练神经电脑翻译任务的模型。这项工作的主要贡献在培训NMT模型期间评估不同的数据增强方法。我提出了5种不同的增强方法，这些方法是结构感知的，这意味着而不是随机选择用于消隐或替换的单词，句子的依赖树用作增强的基础。我首先关于神经网络的详细文献综述，顺序建模，神经机翻译，依赖解析和数据增强。经过详细的探索性数据分析和Hunglish2语料库的预处理之后，我使用所提出的数据增强技术进行实验。匈牙利语的最佳型号达到了33.9的BLEU得分，而英国匈牙利最好的模型达到了28.6的BLEU得分。

Automatic machine translation (MT) metrics are widely used to distinguish the translation qualities of machine translation systems across relatively large test sets (system-level evaluation). However, it is unclear if automatic metrics are reliable at distinguishing good translations from bad translations at the sentence level (segment-level evaluation). In this paper, we investigate how useful MT metrics are at detecting the success of a machine translation component when placed in a larger platform with a downstream task. We evaluate the segment-level performance of the most widely used MT metrics (chrF, COMET, BERTScore, etc.) on three downstream cross-lingual tasks (dialogue state tracking, question answering, and semantic parsing). For each task, we only have access to a monolingual task-specific model. We calculate the correlation between the metric's ability to predict a good/bad translation with the success/failure on the final task for the Translate-Test setup. Our experiments demonstrate that all metrics exhibit negligible correlation with the extrinsic evaluation of the downstream outcomes. We also find that the scores provided by neural metrics are not interpretable mostly because of undefined ranges. Our analysis suggests that future MT metrics be designed to produce error labels rather than scores to facilitate extrinsic evaluation.

对应用深神网络自动解释和分析12铅心电图（ECG）的兴趣增加了。机器学习方法的当前范例通常受到标记数据量的限制。对于临床上的数据，这种现象尤其有问题，在该数据中，根据所需的专业知识和人类努力，规模标签可能是耗时且昂贵的。此外，深度学习分类器可能容易受到对抗性例子和扰动的影响，例如在医疗，临床试验或保险索赔的背景下应用时，可能会带来灾难性的后果。在本文中，我们提出了一种受生理启发的数据增强方法，以提高性能并根据ECG信号提高心脏病检测的鲁棒性。我们通过将数据分布驱动到瓦斯坦斯坦空间中的大地测量中的其他类别来获得增强样品。为了更好地利用领域特定的知识，我们设计了一个基础指标，该指标识别基于生理确定的特征的ECG信号之间的差异。从12铅ECG信号中学习，我们的模型能够区分五种心脏条件。我们的结果表明，准确性和鲁棒性的提高，反映了我们数据增强方法的有效性。

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”。

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。

State-of-the-art machine translation evaluation metrics are based on black-box language models. Hence, recent works consider their explainability with the goals of better understandability for humans and better metric analysis, including failure cases. In contrast, we explicitly leverage explanations to boost the metrics' performance. In particular, we perceive explanations as word-level scores, which we convert, via power means, into sentence-level scores. We combine this sentence-level score with the original metric to obtain a better metric. Our extensive evaluation and analysis across 5 datasets, 5 metrics and 4 explainability techniques shows that some configurations reliably improve the original metrics' correlation with human judgment. On two held datasets for testing, we obtain improvements in 15/18 resp. 4/4 cases. The gains in Pearson correlation are up to 0.032 resp. 0.055. We make our code available.

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。

最近，多模式机器翻译（MMT）的研究激增，其中其他模式（例如图像）用于提高文本系统的翻译质量。这种多模式系统的特殊用途是同时机器翻译的任务，在该任务中，已证明视觉上下文可以补充源句子提供的部分信息，尤其是在翻译的早期阶段。在本文中，我们提出了第一个基于变压器的同时MMT体系结构，该体系结构以前尚未在现场探索过。此外，我们使用辅助监督信号扩展了该模型，该信号使用标记的短语区域比对来指导其视觉注意机制。我们在三个语言方向上进行全面的实验，并使用自动指标和手动检查进行彻底的定量和定性分析。我们的结果表明，（i）监督视觉注意力一致地提高了MMT模型的翻译质量，并且（ii）通过监督损失对MMT进行微调，比从SCRATCH训练MMT的MMT可以提高性能。与最先进的模型相比，我们提出的模型可实现多达2.3 bleu和3.5 Meteor点的改善。

Transfer learning, where a model is first pre-trained on a data-rich task before being finetuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts all text-based language problems into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled data sets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new "Colossal Clean Crawled Corpus", we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our data set, pre-trained models, and code.

我们介绍了IST和Unmabel对WMT 2022关于质量估计（QE）的共享任务的共同贡献。我们的团队参与了所有三个子任务：（i）句子和单词级质量预测；（ii）可解释的量化宽松；（iii）关键错误检测。对于所有任务，我们在彗星框架之上构建，将其与OpenKIWI的预测估计架构连接，并为其配备单词级序列标记器和解释提取器。我们的结果表明，在预处理过程中合并参考可以改善下游任务上多种语言对的性能，并且通过句子和单词级别的目标共同培训可以进一步提高。此外，将注意力和梯度信息结合在一起被证明是提取句子级量化量化宽松模型的良好解释的首要策略。总体而言，我们的意见书在几乎所有语言对的所有三个任务中都取得了最佳的结果。

机器学习模型通常会遇到与训练分布不同的样本。无法识别分布（OOD）样本，因此将该样本分配给课堂标签会显着损害模​​型的可靠性。由于其对在开放世界中的安全部署模型的重要性，该问题引起了重大关注。由于对所有可能的未知分布进行建模的棘手性，检测OOD样品是具有挑战性的。迄今为止，一些研究领域解决了检测陌生样本的问题，包括异常检测，新颖性检测，一级学习，开放式识别识别和分布外检测。尽管有相似和共同的概念，但分别分布，开放式检测和异常检测已被独立研究。因此，这些研究途径尚未交叉授粉，创造了研究障碍。尽管某些调查打算概述这些方法，但它们似乎仅关注特定领域，而无需检查不同领域之间的关系。这项调查旨在在确定其共同点的同时，对各个领域的众多著名作品进行跨域和全面的审查。研究人员可以从不同领域的研究进展概述中受益，并协同发展未来的方法。此外，据我们所知，虽然进行异常检测或单级学习进行了调查，但没有关于分布外检测的全面或最新的调查，我们的调查可广泛涵盖。最后，有了统一的跨域视角，我们讨论并阐明了未来的研究线，打算将这些领域更加紧密地融为一体。

神经机器翻译（NMT）是一个开放的词汇问题。结果，处理在培训期间没有出现的单词（又称唱歌外（OOV）单词）长期以来一直是NMT系统的基本挑战。解决此问题的主要方法是字节对编码（BPE），将包括OOV单词在内的单词分为子字段中。在自动评估指标方面，BPE为广泛的翻译任务取得了令人印象深刻的结果。尽管通常假定使用BPE，但NMT系统能够处理OOV单词，但BPE在翻译OOV单词中的有效性尚未明确测量。在本文中，我们研究了BPE在多大程度上成功地翻译了单词级别的OOV单词。我们根据单词类型，段数，交叉注意权重和训练数据中段NGram的段频率分析OOV单词的翻译质量。我们的实验表明，尽管仔细的BPE设置似乎在整个数据集中翻译OOV单词时相当有用，但很大一部分的OOV单词被错误地翻译而成。此外，我们强调了BPE在为特殊案例（例如命名本性和涉及的语言彼此接近的语言）翻译OOV单词中的有效性稍高。

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％。 。

We propose BERTSCORE, an automatic evaluation metric for text generation. Analogously to common metrics, BERTSCORE computes a similarity score for each token in the candidate sentence with each token in the reference sentence. However, instead of exact matches, we compute token similarity using contextual embeddings. We evaluate using the outputs of 363 machine translation and image captioning systems. BERTSCORE correlates better with human judgments and provides stronger model selection performance than existing metrics. Finally, we use an adversarial paraphrase detection task to show that BERTSCORE is more robust to challenging examples when compared to existing metrics.