Inferring accurate posteriors for high-dimensional representations of the brightness of gravitationally-lensed sources is a major challenge, in part due to the difficulties of accurately quantifying the priors. Here, we report the use of a score-based model to encode the prior for the inference of undistorted images of background galaxies. This model is trained on a set of high-resolution images of undistorted galaxies. By adding the likelihood score to the prior score and using a reverse-time stochastic differential equation solver, we obtain samples from the posterior. Our method produces independent posterior samples and models the data almost down to the noise level. We show how the balance between the likelihood and the prior meet our expectations in an experiment with out-of-distribution data.

Abstractive dialogue summarization has long been viewed as an important standalone task in natural language processing, but no previous work has explored the possibility of whether abstractive dialogue summarization can also be used as a means to boost an NLP system's performance on other important dialogue comprehension tasks. In this paper, we propose a novel type of dialogue summarization task - STRUctured DiaLoguE Summarization - that can help pre-trained language models to better understand dialogues and improve their performance on important dialogue comprehension tasks. We further collect human annotations of STRUDEL summaries over 400 dialogues and introduce a new STRUDEL dialogue comprehension modeling framework that integrates STRUDEL into a graph-neural-network-based dialogue reasoning module over transformer encoder language models to improve their dialogue comprehension abilities. In our empirical experiments on two important downstream dialogue comprehension tasks - dialogue question answering and dialogue response prediction - we show that our STRUDEL dialogue comprehension model can significantly improve the dialogue comprehension performance of transformer encoder language models.

Lack of factual correctness is an issue that still plagues state-of-the-art summarization systems despite their impressive progress on generating seemingly fluent summaries. In this paper, we show that factual inconsistency can be caused by irrelevant parts of the input text, which act as confounders. To that end, we leverage information-theoretic measures of causal effects to quantify the amount of confounding and precisely quantify how they affect the summarization performance. Based on insights derived from our theoretical results, we design a simple multi-task model to control such confounding by leveraging human-annotated relevant sentences when available. Crucially, we give a principled characterization of data distributions where such confounding can be large thereby necessitating the use of human annotated relevant sentences to generate factual summaries. Our approach improves faithfulness scores by 20\% over strong baselines on AnswerSumm \citep{fabbri2021answersumm}, a conversation summarization dataset where lack of faithfulness is a significant issue due to the subjective nature of the task. Our best method achieves the highest faithfulness score while also achieving state-of-the-art results on standard metrics like ROUGE and METEOR. We corroborate these improvements through human evaluation.

文本分割旨在将文本分为连续的语义连贯段，而段标签则与每个段的生成标签有关。过去的工作表明，在解决文档和对话的分段和标签方面取得了成功。通过特定于任务的管道，受监督和无监督的学习目标的结合，这是可能的。在这项工作中，我们提出了一个单一的编码器神经网络，该网络可以处理长文档和对话，同时仅使用标准监督进行细分和细分标记。我们成功地展示了将组合任务作为纯生成任务解决的方法，我们称之为结构化摘要。我们将相同的技术应用于文档和对话数据，并在高资产设置和低资源设置下显示了各个数据集的最新技术性能。我们的结果确定了一个有力的案例，可以考虑整体文本细分和细分标签，并朝着不依赖域专业知识或特定于任务的组件的通用技术迈进。

我们提出了一项实证研究，以适应现有的经过验证的文本对文本模型，以备长期输入。通过沿预训练管道的三个轴的全面研究 - 模型架构，优化目标和训练式语料库，我们提出了一种有效的食谱，以从现有的短篇小说模型中构建长篇小说模型。具体而言，我们用汇总仪的块关注替换了变压器中的全部注意力，并使用蒙版的跨度预测任务为模型预算，长度不同。就训练训练的语料库而言，我们发现，与使用通常在其域覆盖范围中通常受到限制的现有长文档语料库相比，使用大型开放域语料库的随机串联的短篇小说可以提高性能。通过这些发现，我们建立了一个长篇文本模型，该模型可以在长篇文本质量检查任务上实现竞争性能，并在五个长文本摘要数据集上建立新的最新技术，通常优于先前的方法，具有较大的模型大小。

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/

Recommender systems can strongly influence which information we see online, e.g., on social media, and thus impact our beliefs, decisions, and actions. At the same time, these systems can create substantial business value for different stakeholders. Given the growing potential impact of such AI-based systems on individuals, organizations, and society, questions of fairness have gained increased attention in recent years. However, research on fairness in recommender systems is still a developing area. In this survey, we first review the fundamental concepts and notions of fairness that were put forward in the area in the recent past. Afterward, through a review of more than 150 scholarly publications, we present an overview of how research in this field is currently operationalized, e.g., in terms of general research methodology, fairness measures, and algorithmic approaches. Overall, our analysis of recent works points to specific research gaps. In particular, we find that in many research works in computer science, very abstract problem operationalizations are prevalent, and questions of the underlying normative claims and what represents a fair recommendation in the context of a given application are often not discussed in depth. These observations call for more interdisciplinary research to address fairness in recommendation in a more comprehensive and impactful manner.

生成摘要中的事实不一致严重限制了抽象对话摘要的实际应用。尽管通过使用预先训练的模型实现了显着进展，但在人类评估期间发现了大量的幻觉含量。预先接受的模型最常见的是微调文本摘要的跨熵损失，这可能不是最佳策略。在这项工作中，我们为带注释数据提供了事实错误的类型，以突出显示错误的类型并远离对事实的二进制了解。我们进一步提出了一种培训策略，通过新颖的对比微调，改善了摘要的事实一致性和整体素质。基于我们的语言信息的错误类型，我们设计了各个目标的不同模块化目标。具体而言，我们利用硬阴性样本具有误差，以减少事实不一致的产生。为了捕获扬声器之间的关键信息，我们还设计了特定于对话的损失。使用人类评估和自动忠实度量指标，我们表明我们的模型在对话摘要，Samsum语料库中大大降低了各种事实错误。此外，我们的模型可以推广到会议概述，AMI语料库，它产生的分数明显高于两个数据集关于单词 - 重叠度量标准的基线。

许多NLP任务需要处理超出预磨模模型的长度限制的长语境。为了将这些模型扩展到更长的文本序列，已经提出了许多有效的远程注意力变体。尽管沿着这个方向进行了丰富的研究，但仍然难以在实际用例中衡量这些模型的相对有效性，例如，如果我们在预先rain-yfetune范式之后应用这些模型。在这项工作中，我们的目标是对这些具有大规模和受控实验的这些新兴模型进行彻底的分析。对于每个关注变体，我们使用相同的长DOC语料库，然后使用相同的长DOC语料库，然后为现实世界的长情节任务进行芬特这些模型。我们的调查结果揭示了现有广泛使用的远程基准的陷阱，并显示任何经过测试的高效关注可以在标准预介质范式下击败一个简单的本地窗口关注。对本地注意力变化的进一步分析表明，即使是常用的注意力窗口重叠也没有必要实现良好的下游结果 - 使用不相交的本地关注，我们能够构建符合性能的更简单且更高效的Long-Doc QA模型霍尔福勒〜\ citep {longformer}其预先花费的一半。

当前适用于摘要的预训练模型容易出现事实矛盾，这些不一致性歪曲了源文本或介绍无关信息。因此，在我们开发改进的模型时，必须比较摘要的事实一致性。但是，事实一致性的最佳人类评估设置尚未标准化。为了解决这个问题，我们使用基于评分的李克特量表和基于排名的最佳缩放协议对事实一致性进行了评估，对来自CNN每日邮件和XSUM数据集的100篇文章以及四个最新的最新最新的XSUM数据集进行了评估。艺术模型，以确定最可靠的评估框架。我们发现，基于排名的协议提供了整个数据集的摘要质量的更可靠度量，而Likert评分的可靠性取决于目标数据集和评估设计。我们的众包模板和摘要评估将公开获得，以促进对摘要中事实一致性的未来研究。