The state-of-the-art language model-based automatic metrics, e.g. BARTScore, benefiting from large-scale contextualized pre-training, have been successfully used in a wide range of natural language generation (NLG) tasks, including machine translation, text summarization, and data-to-text. Recent studies show that considering both major errors (e.g. mistranslated tokens) and minor errors (e.g. imperfections in fluency) can produce high-quality human judgments. This inspires us to approach the final goal of the evaluation metrics (human-like evaluations) by automatic error analysis. To this end, we augment BARTScore by incorporating the human-like error analysis strategies, namely BARTScore++, where the final score consists of both the evaluations of major errors and minor errors. Experimental results show that BARTScore++ can consistently improve the performance of vanilla BARTScore and outperform existing top-scoring metrics in 20 out of 25 test settings. We hope our technique can also be extended to other pre-trained model-based metrics. We will release our code and scripts to facilitate the community.

我们假设现有的句子级机器翻译（MT）指标在人类参考包含歧义时会效率降低。为了验证这一假设，我们提出了一种非常简单的方法，用于扩展预审计的指标以在文档级别合并上下文。我们将我们的方法应用于三个流行的指标，即Bertscore，Prism和Comet，以及无参考的公制Comet-QE。我们使用提供的MQM注释评估WMT 2021指标共享任务的扩展指标。我们的结果表明，扩展指标的表现在约85％的测试条件下优于其句子级别的级别，而在排除低质量人类参考的结果时。此外，我们表明我们的文档级扩展大大提高了其对话语现象任务的准确性，从而优于专用基线高达6.1％。我们的实验结果支持我们的初始假设，并表明对指标的简单扩展使他们能够利用上下文来解决参考中的歧义。

Modern embedding-based metrics for evaluation of generated text generally fall into one of two paradigms: discriminative metrics that are trained to directly predict which outputs are of higher quality according to supervised human annotations, and generative metrics that are trained to evaluate text based on the probabilities of a generative model. Both have their advantages; discriminative metrics are able to directly optimize for the problem of distinguishing between good and bad outputs, while generative metrics can be trained using abundant raw text. In this paper, we present a framework that combines the best of both worlds, using both supervised and unsupervised signals from whatever data we have available. We operationalize this idea by training T5Score, a metric that uses these training signals with mT5 as the backbone. We perform an extensive empirical comparison with other existing metrics on 5 datasets, 19 languages and 280 systems, demonstrating the utility of our method. Experimental results show that: T5Score achieves the best performance on all datasets against existing top-scoring metrics at the segment level. We release our code and models at https://github.com/qinyiwei/T5Score.

最近提出的基于BERT的评估指标在标准评估基准方面表现良好，但容易受到对抗性攻击的影响，例如与事实错误有关。我们认为这（部分原因）是因为它们是语义相似性的模型。相反，我们根据自然语言推断（NLI）制定评估指标，我们认为这是更合适的建模。我们设计了一个基于偏好的对抗攻击框架，并表明我们的基于NLI的指标比最近基于BERT的指标更强大。在标准基准上，我们的基于NLI的指标的表现优于现有的摘要指标，但在SOTA MT指标下执行。但是，当我们将现有指标与NLI指标相结合时，我们可以获得更高的对抗性鲁棒性（ +20％至 +30％）和较高质量的指标，如标准基准测量（ +5％至 +25％）。

文本生成的广泛使用的评估指标要么与更长的文本效果不错，要么无法评估文本质量的所有方面。在本文中，我们引入了一个名为SMART的新指标，以减轻此类限制。具体而言，我们将句子视为匹配的基本单位，而不是代币，并使用句子匹配函数来匹配匹配候选和参考句子。还将候选句子与源文件中的句子进行了比较，以允许接地（例如，事实）评估。我们的结果表明，我们提出的指标与基于模型的匹配函数的系统级相关性优于萨姆瓦尔摘要元评估数据集上的所有竞争指标指标。后者不使用任何神经模型，这在模型开发阶段很有用，在这些阶段，资源可以受到限制且需要快速评估。最后，我们还进行了广泛的分析，表明我们提出的指标与较长的摘要很好地运行，并且对特定模型的偏见较小。

Is it possible to leverage large scale raw and raw parallel corpora to build a general learned metric? Existing learned metrics have gaps to human judgements, are model-dependent or are limited to the domains or tasks where human ratings are available. In this paper, we propose SEScore2, a model-based metric pretrained over million-scale synthetic dataset constructed by our novel retrieval augmented data synthesis pipeline. SEScore2 achieves high correlation to human judgements without any human rating supervisions. Importantly, our unsupervised SEScore2 can outperform supervised metrics, which are trained on the News human ratings, at the TED domain. We evaluate SEScore2 over four text generation tasks across three languages. SEScore2 outperforms all prior unsupervised evaluation metrics in machine translation, speech translation, data-to-text and dialogue generation, with average Kendall improvements 0.158. SEScore2 even outperforms SOTA supervised BLEURT at data-to-text, dialogue generation and overall correlation.

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。

The rapid growth of machine translation (MT) systems has necessitated comprehensive studies to meta-evaluate evaluation metrics being used, which enables a better selection of metrics that best reflect MT quality. Unfortunately, most of the research focuses on high-resource languages, mainly English, the observations for which may not always apply to other languages. Indian languages, having over a billion speakers, are linguistically different from English, and to date, there has not been a systematic study of evaluating MT systems from English into Indian languages. In this paper, we fill this gap by creating an MQM dataset consisting of 7000 fine-grained annotations, spanning 5 Indian languages and 7 MT systems, and use it to establish correlations between annotator scores and scores obtained using existing automatic metrics. Our results show that pre-trained metrics, such as COMET, have the highest correlations with annotator scores. Additionally, we find that the metrics do not adequately capture fluency-based errors in Indian languages, and there is a need to develop metrics focused on Indian languages. We hope that our dataset and analysis will help promote further research in this area.

这项工作适用于最低贝叶斯风险（MBR）解码，以优化翻译质量的各种自动化指标。机器翻译中的自动指标最近取得了巨大的进步。特别是，在人类评级（例如BLEurt，或Comet）上微调，在与人类判断的相关性方面是优于表面度量的微调。我们的实验表明，神经翻译模型与神经基于基于神经参考度量，BLEURT的组合导致自动和人类评估的显着改善。通过与经典光束搜索输出不同的翻译获得该改进：这些翻译的可能性较低，并且较少受到Bleu等表面度量的青睐。

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.

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.

Natural Language Generation (NLG) has improved exponentially in recent years thanks to the development of sequence-to-sequence deep learning technologies such as Transformer-based language models. This advancement has led to more fluent and coherent NLG, leading to improved development in downstream tasks such as abstractive summarization, dialogue generation and data-to-text generation. However, it is also apparent that deep learning based generation is prone to hallucinate unintended text, which degrades the system performance and fails to meet user expectations in many real-world scenarios. To address this issue, many studies have been presented in measuring and mitigating hallucinated texts, but these have never been reviewed in a comprehensive manner before. In this survey, we thus provide a broad overview of the research progress and challenges in the hallucination problem in NLG. The survey is organized into two parts: (1) a general overview of metrics, mitigation methods, and future directions; and (2) an overview of task-specific research progress on hallucinations in the following downstream tasks, namely abstractive summarization, dialogue generation, generative question answering, data-to-text generation, machine translation, and visual-language generation. This survey serves to facilitate collaborative efforts among researchers in tackling the challenge of hallucinated texts in NLG.

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％。 。

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.

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。

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.

In this work, we explore a useful but often neglected methodology for robustness analysis of text generation evaluation metrics: stress tests with synthetic data. Basically, we design and synthesize a wide range of potential errors and check whether they result in a commensurate drop in the metric scores. We examine a range of recently proposed evaluation metrics based on pretrained language models, for the tasks of open-ended generation, translation, and summarization. Our experiments reveal interesting insensitivities, biases, or even loopholes in existing metrics. For example, we find that BERTScore ignores truncation errors in summarization, and MAUVE (built on top of GPT-2) is insensitive to errors at the beginning of generations. Further, we investigate the reasons behind these blind spots and suggest practical workarounds for a more reliable evaluation of text generation.

自然语言处理研究人员已经确定了对生成任务的评估方法的局限性，具有新的问题，提出了自动指标和人群判断的有效性。同时，改善生成模型的努力倾向于专注于简单的n-gram重叠度量（例如，Bleu，Rouge）。我们认为，对模型和指标的新进展应该每个人都更直接受益并告知另一个。因此，我们提出了排行榜，竞争排行榜（广告牌）的概括，同时跟踪语言生成任务和指标的进展。与通过预定度量分类提交系统的传统的单向排行榜不同，广告牌可接受发电机和评估度量作为竞争条目。广告牌会自动创建一个基于跨发电机的全局分析选择和线性地组合一些指标的集合度量。此外，指标基于与人类判断的相关性进行排序。我们释放了用于机器翻译，摘要和图像标题的四个广告牌。我们展示了一些多样化度量的线性集合有时会在隔离中显着优于现有的度量。我们的混合效果模型分析表明，大多数自动度量，尤其是基于参考的机器，对人类发电的重估，展示了更新度量的重要性，将来变得更强大（也许与人类更相似）。

长表质疑应答（LFQA）任务要求将相关的文件检索到查询，使用它们形成段落长度答案。尽管LFQA建模相当大，但基本问题妨碍了其进度：i）火车/验证/测试数据集重叠，ii）缺少自动度量标准和III）在检索的文档中产生的答案不会“接地”。这项工作解决了这些关键瓶颈的每一个，有助于自然语言推理/生成（NLI / NLG）方法和指标，使其减轻重大进展。

An optimal delivery of arguments is key to persuasion in any debate, both for humans and for AI systems. This requires the use of clear and fluent claims relevant to the given debate. Prior work has studied the automatic assessment of argument quality extensively. Yet, no approach actually improves the quality so far. Our work is the first step towards filling this gap. We propose the task of claim optimization: to rewrite argumentative claims to optimize their delivery. As an initial approach, we first generate a candidate set of optimized claims using a sequence-to-sequence model, such as BART, while taking into account contextual information. Our key idea is then to rerank generated candidates with respect to different quality metrics to find the best optimization. In automatic and human evaluation, we outperform different reranking baselines on an English corpus, improving 60% of all claims (worsening 16% only). Follow-up analyses reveal that, beyond copy editing, our approach often specifies claims with details, whereas it adds less evidence than humans do. Moreover, its capabilities generalize well to other domains, such as instructional texts.