实现通用语言情报是自然语言处理的长期目标,标准评估基准发挥基本和指导作用。我们认为,对于通用语言智能评估,基准本身需要全面和系统。为此,我们提出了Cuge,一种中文语言理解和生成评估基准,具有以下特征:(1)分层基准框架,其中数据集主要选择和组织语言能力 - 任务数据集层次结构。 (2)多级评分策略,其中基于分层框架提供了不同级别的模型性能。为了促进CUGE,我们提供了一个公共排行榜,可以自定义,以支持灵活的模型判断标准。代表性预先训练的语言模型的评估结果表明了对通用语言智能的完善的充足空间。 Cuge在Cuge.baai.ac.cn上公开提供。
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有关应答数据集和模型的研究在研究界中获得了很多关注。其中许多人释放了自己的问题应答数据集以及模型。我们在该研究领域看到了巨大的进展。本调查的目的是识别,总结和分析许多研究人员释放的现有数据集,尤其是在非英语数据集以及研究代码和评估指标等资源中。在本文中,我们审查了问题应答数据集,这些数据集可以以法语,德语,日语,中文,阿拉伯语,俄语以及多语言和交叉的问答数据集进行英语。
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通过自我监督的学习预先训练的大型语言模型在各种各样的任务上表现出令人印象深刻的零击功能。在这项工作中,我们介绍了Welm:一种针对中文的精心读取的预训练的语言模型,能够无缝执行不同类型的任务,以零或几次演示。 Welm通过“阅读”涵盖广泛主题的精选高质量语料库来接受10b参数的培训。我们表明,韦尔姆拥有有关各种领域和语言的广泛知识。在18个单语(中文)任务中,WELM可以大大优于现有的预训练模型,尺寸相似,并匹配高达25倍大的模型的性能。韦尔姆还表现出强大的多种语言和代码转换理解的能力,优于预先对30种语言进行预培训的现有多语言模型。此外,我们收集了人工编写的提示,并通过多次培训进行了大量的中文和微调韦尔姆的监督数据集。最终的模型可以实现对看不见的任务类型的强烈概括,并在零射门学习中优于无监督的韦尔姆。最后,我们证明韦尔姆具有解释和校准自己的决策的基本技能,这可能是未来研究的有希望的方向。我们的模型可以从https://welm.weixin.qq.com/docs/api/应用。
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本文对过去二十年来对自然语言生成(NLG)的研究提供了全面的审查,特别是与数据到文本生成和文本到文本生成深度学习方法有关,以及NLG的新应用技术。该调查旨在(a)给出关于NLG核心任务的最新综合,以及该领域采用的建筑;(b)详细介绍各种NLG任务和数据集,并提请注意NLG评估中的挑战,专注于不同的评估方法及其关系;(c)强调一些未来的强调和相对近期的研究问题,因为NLG和其他人工智能领域的协同作用而增加,例如计算机视觉,文本和计算创造力。
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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.
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问答系统被认为是流行且经常有效的信息在网络上寻求信息的手段。在这样的系统中,寻求信息者可以通过自然语言提出问题来获得对他们的查询的简短回应。交互式问题回答是一种最近提出且日益流行的解决方案,它位于问答和对话系统的交集。一方面,用户可以以普通语言提出问题,并找到对她的询问的实际回答;另一方面,如果在初始请求中有多个可能的答复,很少或歧义,则系统可以将问题交通会话延长到对话中。通过允许用户提出更多问题,交互式问题回答使用户能够与系统动态互动并获得更精确的结果。这项调查提供了有关当前文献中普遍存在的交互式提问方法的详细概述。它首先要解释提问系统的基本原理,从而定义新的符号和分类法,以将所有已确定的作品结合在统一框架内。然后,根据提出的方法,评估方法和数据集/应用程序域来介绍和检查有关交互式问题解答系统的审查已发表的工作。我们还描述了围绕社区提出的特定任务和问题的趋势,从而阐明了学者的未来利益。 GitHub页面的综合综合了本文献研究中涵盖的所有主要主题,我们的工作得到了进一步的支持。 https://sisinflab.github.io/interactive-question-answering-systems-survey/
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We present TriviaQA, a challenging reading comprehension dataset containing over 650K question-answer-evidence triples. TriviaQA includes 95K questionanswer pairs authored by trivia enthusiasts and independently gathered evidence documents, six per question on average, that provide high quality distant supervision for answering the questions. We show that, in comparison to other recently introduced large-scale datasets, TriviaQA (1) has relatively complex, compositional questions, (2) has considerable syntactic and lexical variability between questions and corresponding answer-evidence sentences, and (3) requires more cross sentence reasoning to find answers. We also present two baseline algorithms: a featurebased classifier and a state-of-the-art neural network, that performs well on SQuAD reading comprehension. Neither approach comes close to human performance (23% and 40% vs. 80%), suggesting that Trivi-aQA is a challenging testbed that is worth significant future study. 1
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预先接受的语言模型实现了最先进的导致各种自然语言处理(NLP)任务。 GPT-3表明,缩放预先训练的语言模型可以进一步利用它们的巨大潜力。最近提出了一个名为Ernie 3.0的统一框架,以预先培训大型知识增强型号,并培训了具有10亿参数的模型。 Ernie 3.0在各种NLP任务上表现出最先进的模型。为了探讨缩放的表现,我们培养了百卢比的3.0泰坦参数型号,在PaddlePaddle平台上有高达260亿参数的泰坦。此外,我们设计了一种自我监督的对抗性损失和可控语言建模损失,以使ERNIE 3.0 TITAN产生可信和可控的文本。为了减少计算开销和碳排放,我们向Ernie 3.0泰坦提出了一个在线蒸馏框架,教师模型将同时教授学生和培训。埃塞尼3.0泰坦是迄今为止最大的中国密集预训练模型。经验结果表明,Ernie 3.0泰坦在68个NLP数据集中优于最先进的模型。
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The internet has had a dramatic effect on the healthcare industry, allowing documents to be saved, shared, and managed digitally. This has made it easier to locate and share important data, improving patient care and providing more opportunities for medical studies. As there is so much data accessible to doctors and patients alike, summarizing it has become increasingly necessary - this has been supported through the introduction of deep learning and transformer-based networks, which have boosted the sector significantly in recent years. This paper gives a comprehensive survey of the current techniques and trends in medical summarization
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在本文中,我们介绍了基于大型预训练的语言模型(PLM)pangu-alpha(Zeng等,2021)的中国预训练的开放域对话生成模型。与其他对大量对话数据进行培训的预训练的对话模型不同,我们旨在通过继承PLM的有价值的语言能力和知识来构建强大的对话模型,并以相对较少的数据和计算成本构建强大的对话模型。为此,我们训练大型PLM Pangu-Alpha的Pangu-bot,该机器人已被证明在各种中国自然语言任务上表现出色。我们研究了pangu-bot产生的响应的不同方面,包括响应质量,知识和安全性。我们表明,Pangu-Bot优于最先进的中国对话系统(CDIALGPT(Wang等,2020),Eva(Zhou等,2021),EVA2.0(Gu等,2022)) W.R.T.以上三个方面。我们还证明,可以轻松地部署pangu-bot,以在没有进一步训练的情况下产生情感反应。在整个经验分析中,我们还指出,Pangu-bot响应质量,知识正确性和安全性仍然远非完美,进一步的探索对于建立可靠且智能的对话系统是必不可少的。我们的型号和代码将在https://github.com/huawei-noah/pretretaining-language-model/tree/master/master/pangu-bot上提供。
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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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大型预训练的语言模型已经表现出了产生现实文本的强大功能。但是,控制生成结果仍然具有挑战性。以前的方法,例如提示远远不足,这限制了语言模型的使用。为了解决这一挑战,我们提出了一种创新的方法,逆提示,更好地控制文本生成。逆提示的核心思想是使用生成的文本来在波束搜索期间反转提示,这增强了提示和生成文本之间的相关性,并提供了更好的可控性。经验上,我们预先培训了大规模的汉语模型,在开放式诗歌生成和开放式长形问题的任务上使用人力评估进行系统研究。我们的研究结果表明,我们的提出方法显着优于基线,而我们的发电质量与某些任务中的某些任务接近人类性能。叙述者可以在https://pretrain.aminer.cn/apps/poetry.html上尝试我们的诗歌生成演示,而我们的QA演示可以在https://pretrain.aminer.cn/app/qa找到。对于研究人员来说,代码是在https://github.com/thudm/inverseprompting中提供的。
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For natural language understanding (NLU) technology to be maximally useful, it must be able to process language in a way that is not exclusive to a single task, genre, or dataset. In pursuit of this objective, we introduce the General Language Understanding Evaluation (GLUE) benchmark, a collection of tools for evaluating the performance of models across a diverse set of existing NLU tasks. By including tasks with limited training data, GLUE is designed to favor and encourage models that share general linguistic knowledge across tasks. GLUE also includes a hand-crafted diagnostic test suite that enables detailed linguistic analysis of models. We evaluate baselines based on current methods for transfer and representation learning and find that multi-task training on all tasks performs better than training a separate model per task. However, the low absolute performance of our best model indicates the need for improved general NLU systems.
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问题回答(QA)是最重要的自然语言处理(NLP)任务之一。它旨在使用NLP技术根据大规模的非结构化语料库生成对给定问题的相应答案。随着深度学习的发展,正在提出越来越具有挑战性的质量检查数据集,并且许多用于解决它们的新方法也正在出现。在本文中,我们研究了在深度学习时代发布的有影响力的质量检查数据集。具体来说,我们首先引入两个最常见的质量检查任务 - 文本问题答案和视觉问题 - 分别涵盖最具代表性的数据集,然后给出质量检查研究的一些当前挑战。
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We introduce a large scale MAchine Reading COmprehension dataset, which we name MS MARCO. The dataset comprises of 1,010,916 anonymized questionssampled from Bing's search query logs-each with a human generated answer and 182,669 completely human rewritten generated answers. In addition, the dataset contains 8,841,823 passages-extracted from 3,563,535 web documents retrieved by Bing-that provide the information necessary for curating the natural language answers. A question in the MS MARCO dataset may have multiple answers or no answers at all. Using this dataset, we propose three different tasks with varying levels of difficulty: (i) predict if a question is answerable given a set of context passages, and extract and synthesize the answer as a human would (ii) generate a well-formed answer (if possible) based on the context passages that can be understood with the question and passage context, and finally (iii) rank a set of retrieved passages given a question. The size of the dataset and the fact that the questions are derived from real user search queries distinguishes MS MARCO from other well-known publicly available datasets for machine reading comprehension and question-answering. We believe that the scale and the real-world nature of this dataset makes it attractive for benchmarking machine reading comprehension and question-answering models.
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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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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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我们介绍了MTG,这是一套新的基准套件,用于培训和评估多语言文本生成。它是具有最大人类通知数据(400K)的第一次传播的多语言多路文本生成数据集。它包括五种语言(英语,德语,法语,西班牙语和中文)的四代任务(故事产生,问题生成,标题生成和文本摘要)。Multiway设置可以启用跨语言和任务的模型测试知识传输功能。使用MTG,我们从不同方面训练和分析了几种流行的多语言生成模型。我们的基准套件通过更多的人为宣传的并行数据促进了模型性能增强。它提供了各种一代方案的全面评估。代码和数据可在\ url {https://github.com/zide05/mtg}上获得。
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过去十年互联网上可用的信息和信息量增加。该数字化导致自动应答系统需要从冗余和过渡知识源中提取富有成效的信息。这些系统旨在利用自然语言理解(NLU)从此巨型知识源到用户查询中最突出的答案,从而取决于问题答案(QA)字段。问题答案涉及但不限于用户问题映射的步骤,以获取相关查询,检索相关信息,从检索到的信息等找到最佳合适的答案等。当前对深度学习模型的当前改进估计所有这些任务的令人信服的性能改进。在本综述工作中,根据问题的类型,答案类型,证据答案来源和建模方法进行分析QA场的研究方向。此细节随后是自动问题生成,相似性检测和语言的低资源可用性等领域的开放挑战。最后,提出了对可用数据集和评估措施的调查。
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Multi-hop Machine reading comprehension is a challenging task with aim of answering a question based on disjoint pieces of information across the different passages. The evaluation metrics and datasets are a vital part of multi-hop MRC because it is not possible to train and evaluate models without them, also, the proposed challenges by datasets often are an important motivation for improving the existing models. Due to increasing attention to this field, it is necessary and worth reviewing them in detail. This study aims to present a comprehensive survey on recent advances in multi-hop MRC evaluation metrics and datasets. In this regard, first, the multi-hop MRC problem definition will be presented, then the evaluation metrics based on their multi-hop aspect will be investigated. Also, 15 multi-hop datasets have been reviewed in detail from 2017 to 2022, and a comprehensive analysis has been prepared at the end. Finally, open issues in this field have been discussed.
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