通过将搜索任务框架作为解释跨度检测来绘制语义搜索问题，即给定文本作为查询短语的段，任务是在给定文档中识别其释放，与通常相同的建模设置用于提取问题的回答。在Turku释放语料库中的100,000手动提取芬兰语释义对，包括其原始文档上下文，我们发现我们的扫描跨度检测模型分别优于31.9pp和22.4pp的两个强烈的检索基线（词汇相似性和BERT句子嵌入）。匹配，达到22.3pp和12.9pp的令牌级F分数。这展示了在跨度检索而不是句子相似性方面建模任务的强大优点。此外，我们介绍了一种通过背部翻译创建人工释义数据的方法，适用于手动注释用于训练的跨度检测模型的剖析资源。

问题答案（QA）是自然语言处理中最具挑战性的最具挑战性的问题之一（NLP）。问答（QA）系统试图为给定问题产生答案。这些答案可以从非结构化或结构化文本生成。因此，QA被认为是可以用于评估文本了解系统的重要研究区域。大量的QA研究致力于英语语言，调查最先进的技术和实现最先进的结果。然而，由于阿拉伯QA中的研究努力和缺乏大型基准数据集，在阿拉伯语问答进展中的研究努力得到了很大速度的速度。最近许多预先接受的语言模型在许多阿拉伯语NLP问题中提供了高性能。在这项工作中，我们使用四个阅读理解数据集来评估阿拉伯QA的最先进的接种变压器模型，它是阿拉伯语 - 队，ArcD，AQAD和TYDIQA-GoldP数据集。我们微调并比较了Arabertv2基础模型，ArabertV0.2大型型号和ARAElectra模型的性能。在最后，我们提供了一个分析，了解和解释某些型号获得的低绩效结果。

In this work, we introduce IndicXTREME, a benchmark consisting of nine diverse tasks covering 18 languages from the Indic sub-continent belonging to four different families. Across languages and tasks, IndicXTREME contains a total of 103 evaluation sets, of which 51 are new contributions to the literature. To maintain high quality, we only use human annotators to curate or translate\footnote{for IndicXParaphrase, where an automatic translation system is used, a second human verification and correction step is done.} our datasets. To the best of our knowledge, this is the first effort toward creating a standard benchmark for Indic languages that aims to test the zero-shot capabilities of pretrained language models. We also release IndicCorp v2, an updated and much larger version of IndicCorp that contains 20.9 billion tokens in 24 languages. We pretrain IndicBERT v2 on IndicCorp v2 and evaluate it on IndicXTREME to show that it outperforms existing multilingual language models such as XLM-R and MuRIL.

自Bert（Devlin等，2018）以来，学习上下文化的单词嵌入一直是NLP中的事实上的标准。然而，学习上下文化短语嵌入的进展受到缺乏人类通知的语句基准基准的阻碍。为了填补这一空白，我们提出了PIC- 〜28K名词短语的数据集伴随着它们的上下文Wikipedia页面，以及一套三个任务，这些任务增加了评估短语嵌入质量的难度。我们发现，在我们的数据集中进行的培训提高了排名模型的准确性，并明显地将问题答案（QA）模型推向了近人类的准确性，而在语义搜索上，鉴于询问短语和段落，在语义搜索上是95％的精确匹配（EM）。有趣的是，我们发现这种令人印象深刻的性能的证据是因为质量检查模型学会了更好地捕获短语的共同含义，而不管其实际背景如何。也就是说，在我们的短语中歧义歧义（PSD）任务上，SOTA模型的精度大大下降（60％EM），在两个不同情况下未能区分相同短语的两种不同感觉。在我们的3任任务基准测试中的进一步结果表明，学习上下文化的短语嵌入仍然是一个有趣的开放挑战。

近年来，低资源机器阅读理解（MRC）取得了重大进展，模型在各种语言数据集中获得了显着性能。但是，这些模型都没有为URDU语言定制。这项工作探讨了通过将机器翻译的队伍与来自剑桥O级书籍的Wikipedia文章和Urdu RC工作表组合的人生成的样本组合了机器翻译的小队，探讨了乌尔通题的半自动创建了数据集（UQuad1.0）。 UQuad1.0是一个大型URDU数据集，用于提取机器阅读理解任务，由49K问题答案成对组成，段落和回答格式。在UQuad1.0中，通过众包的原始SquAd1.0和大约4000对的机器翻译产生45000对QA。在本研究中，我们使用了两种类型的MRC型号：基于规则的基线和基于先进的变换器的模型。但是，我们发现后者优于其他人;因此，我们已经决定专注于基于变压器的架构。使用XLMroberta和多语言伯特，我们分别获得0.66和0.63的F1得分。

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”。

Open-domain question answering relies on efficient passage retrieval to select candidate contexts, where traditional sparse vector space models, such as TF-IDF or BM25, are the de facto method. In this work, we show that retrieval can be practically implemented using dense representations alone, where embeddings are learned from a small number of questions and passages by a simple dualencoder framework. When evaluated on a wide range of open-domain QA datasets, our dense retriever outperforms a strong Lucene-BM25 system greatly by 9%-19% absolute in terms of top-20 passage retrieval accuracy, and helps our end-to-end QA system establish new state-of-the-art on multiple open-domain QA benchmarks. 1 * Equal contribution 1 The code and trained models have been released at https://github.com/facebookresearch/DPR.

我们介绍了关于多语言信息访问（MIA）2022共享任务的研讨会的结果，评估了16种类型上多样性的语言中的跨语性开放回程答案（QA）系统。在此任务中，我们在14种类型上多样化的语言中调整了两个大规模的跨语性开放式质疑QA数据集，并使用了2种代表性不足的语言中的新注释的开放式QA数据：Tagalog和Tamil。四个团队提交了他们的系统。利用迭代开采的最佳系统是不同的负面示例和较大的预审慎模型达到32.2 F1，表现优于我们的基线4.5分。第二最佳系统使用实体感知的上下文化表示文档检索，并在泰米尔语（20.8 F1）方面取得了重大改进，而其他大多数系统的得分几乎为零。

本文介绍了一个大规模的多模式和多语言数据集，该数据集旨在促进在语言中的上下文使用中对图像进行接地的研究。数据集由选择明确说明在电影字幕句子中表达的概念的图像组成。数据集是一个宝贵的资源，因为（i）图像与文本片段一致，而不是整个句子； （ii）对于文本片段和句子，可以使用多个图像； （iii）这些句子是自由形式和现实世界的； （iv）平行文本是多语言的。我们为人类设置了一个填充游戏，以评估数据集的自动图像选择过程的质量。我们在两个自动任务上显示了数据集的实用程序：（i）填充填充； （ii）词汇翻译。人类评估和自动模型的结果表明，图像可以是文本上下文的有用补充。该数据集将受益于单词视觉基础的研究，尤其是在自由形式句子的背景下，可以从https://doi.org/10.5281/zenodo.5034604获得创意常识许可。

We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a;Radford et al., 2018), BERT is designed to pretrain deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be finetuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial taskspecific architecture modifications.BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).

两个关键假设塑造了排名检索的通常视图：（1）搜索者可以为他们希望看到的文档中的疑问选择单词，并且（2）排名检索的文档就足以，因为搜索者将足够就足够了能够认识到他们希望找到的那些。当要搜索的文档处于搜索者未知的语言时，既不是真的。在这种情况下，需要跨语言信息检索（CLIR）。本章审查了艺术技术的交流信息检索，并概述了一些开放的研究问题。

Supervised Question Answering systems (QA systems) rely on domain-specific human-labeled data for training. Unsupervised QA systems generate their own question-answer training pairs, typically using secondary knowledge sources to achieve this outcome. Our approach (called PIE-QG) uses Open Information Extraction (OpenIE) to generate synthetic training questions from paraphrased passages and uses the question-answer pairs as training data for a language model for a state-of-the-art QA system based on BERT. Triples in the form of <subject, predicate, object> are extracted from each passage, and questions are formed with subjects (or objects) and predicates while objects (or subjects) are considered as answers. Experimenting on five extractive QA datasets demonstrates that our technique achieves on-par performance with existing state-of-the-art QA systems with the benefit of being trained on an order of magnitude fewer documents and without any recourse to external reference data sources.

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

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.

为英语以外的其他语言构建有效的开放式问题答案（开放质量质量质量）系统可能是具有挑战性的，这主要是由于缺乏标记的培训数据。我们提出了一种数据有效的方法来引导此类系统，以使用英语以外的其他语言。我们的方法只需要在给定语言中，以及机器翻译的数据以及至少一个双语语言模型中有限的质量检查资源。为了评估我们的方法，我们为冰岛语言构建了这样的系统，并评估了Trivia风格数据集的性能。用于培训的语料库是英语的，但机器被翻译成冰岛。我们训练双语的冰岛/英语模型，以嵌入英语背景和冰岛问题，并在用密集酶引入的方法之后（Lee等，2021）。最终的系统是冰岛和英语之间的开放式域杂志系统。最后，该系统适用于仅冰岛开放式质量检查，以说明如何有效地创建开放的QA系统，而使用感兴趣的语言对策划数据集的访问有限。

Recent work on open domain question answering (QA) assumes strong supervision of the supporting evidence and/or assumes a blackbox information retrieval (IR) system to retrieve evidence candidates. We argue that both are suboptimal, since gold evidence is not always available, and QA is fundamentally different from IR. We show for the first time that it is possible to jointly learn the retriever and reader from question-answer string pairs and without any IR system. In this setting, evidence retrieval from all of Wikipedia is treated as a latent variable. Since this is impractical to learn from scratch, we pre-train the retriever with an Inverse Cloze Task. We evaluate on open versions of five QA datasets. On datasets where the questioner already knows the answer, a traditional IR system such as BM25 is sufficient. On datasets where a user is genuinely seeking an answer, we show that learned retrieval is crucial, outperforming BM25 by up to 19 points in exact match.

The widely studied task of Natural Language Inference (NLI) requires a system to recognize whether one piece of text is textually entailed by another, i.e. whether the entirety of its meaning can be inferred from the other. In current NLI datasets and models, textual entailment relations are typically defined on the sentence- or paragraph-level. However, even a simple sentence often contains multiple propositions, i.e. distinct units of meaning conveyed by the sentence. As these propositions can carry different truth values in the context of a given premise, we argue for the need to recognize the textual entailment relation of each proposition in a sentence individually. We propose PropSegmEnt, a corpus of over 35K propositions annotated by expert human raters. Our dataset structure resembles the tasks of (1) segmenting sentences within a document to the set of propositions, and (2) classifying the entailment relation of each proposition with respect to a different yet topically-aligned document, i.e. documents describing the same event or entity. We establish strong baselines for the segmentation and entailment tasks. Through case studies on summary hallucination detection and document-level NLI, we demonstrate that our conceptual framework is potentially useful for understanding and explaining the compositionality of NLI labels.

句子嵌入通常用于文本聚类和语义检索任务中。最先进的句子表示方法基于大量手动标记句子对集合的人工神经网络。高资源语言（例如英语或中文）可以使用足够数量的注释数据。在不太受欢迎的语言中，必须使用多语言模型，从而提供较低的性能。在本出版物中，我们通过提出一种培训有效的语言特定句子编码的方法来解决此问题，而无需手动标记数据。我们的方法是从句子对准双语文本语料库中自动构建释义对数据集。然后，我们使用收集的数据来微调具有附加复发池层的变压器语言模型。我们的句子编码器可以在不到一天的时间内在一张图形卡上进行培训，从而在各种句子级的任务上实现高性能。我们在波兰语中评估了八个语言任务的方法，并将其与最佳可用多语言句子编码器进行比较。

识别跨语言抄袭是挑战性的，特别是对于遥远的语言对和感知翻译。我们介绍了这项任务的新型多语言检索模型跨语言本体论（CL \ nobreakdash-osa）。 CL-OSA表示从开放知识图Wikidata获得的实体向量的文档。反对其他方法，Cl \ nobreakdash-osa不需要计算昂贵的机器翻译，也不需要使用可比较或平行语料库进行预培训。它可靠地歧义同音异义和缩放，以允许其应用于Web级文档集合。我们展示了CL-OSA优于从五个大局部多样化的测试语料中检索候选文档的最先进的方法，包括日语英语等遥控语言对。为了识别在角色级别的跨语言抄袭，CL-OSA主要改善了感觉识别翻译的检测。对于这些挑战性案例，CL-OSA在良好的Plagdet得分方面的表现超过了最佳竞争对手的比例超过两种。我们研究的代码和数据公开可用。

This paper proposes to tackle opendomain question answering using Wikipedia as the unique knowledge source: the answer to any factoid question is a text span in a Wikipedia article. This task of machine reading at scale combines the challenges of document retrieval (finding the relevant articles) with that of machine comprehension of text (identifying the answer spans from those articles). Our approach combines a search component based on bigram hashing and TF-IDF matching with a multi-layer recurrent neural network model trained to detect answers in Wikipedia paragraphs. Our experiments on multiple existing QA datasets indicate that (1) both modules are highly competitive with respect to existing counterparts and (2) multitask learning using distant supervision on their combination is an effective complete system on this challenging task.