In this paper, we seek to measure how much information a component in a neural network could extract from the representations fed into it. Our work stands in contrast to prior probing work, most of which investigates how much information a model's representations contain. This shift in perspective leads us to propose a new principle for probing, the architectural bottleneck principle: In order to estimate how much information a given component could extract, a probe should look exactly like the component. Relying on this principle, we estimate how much syntactic information is available to transformers through our attentional probe, a probe that exactly resembles a transformer's self-attention head. Experimentally, we find that, in three models (BERT, ALBERT, and RoBERTa), a sentence's syntax tree is mostly extractable by our probe, suggesting these models have access to syntactic information while composing their contextual representations. Whether this information is actually used by these models, however, remains an open question.

对话模型面临一个困难的权衡。对模型进行了大量文本培训，但是他们的响应需要仅限于对话框的所需范围和样式。由于用于实现前者包含与后者不兼容的语言的数据集，因此在较小的策划数据集上对预训练的对话框模型进行了微调。但是，微调过程剥夺了他们产生多种反应的能力，最终将他们降低到乏味的对话伙伴。在本文中，我们调查提示是否可以减轻上述权衡。具体来说，我们试验了在查询上调节提示，而不是训练所有查询的单个提示。通过遵循直觉，即冻结预训练的语言模型将保持其表现力，我们发现与微调相比，提示可以达到更高的BLEU得分，并显着改善了响应的多样性和新颖性。

每个法律案件都以以下两种方式之一制定法律。它要么扩大其范围，在这种情况下，它设定了积极的先例，要么将其缩小范围，在这种情况下，它设定了负面的先例。虽然法律结果预测（这是对积极先例的预测，这无非是AI中越来越受欢迎的任务），但我们是第一个通过关注负面结果来调查负面先例预测的人。我们发现了现有模型预测正面和负面结果的能力中的不对称性。如果最新的结果预测模型预测75.06 F1的阳性结果，则他们预测仅在10.09 F1的负结果，比随机基线更糟。为了解决这一绩效差距，我们开发了两个新模型，灵感来自法院流程的动态。我们的第一个模型将积极的预测评分显着提高到77.15 F1，而我们的第二个模型将负结果预测性能翻了一番，达到24.01 F1。尽管有这种改进，但转移到负面结果的重点表明，在建模法律上，仍然有足够的增长空间。

将已知的原始概念重组为更大的新型组合是一种典型的人类认知能力。NLP中的大型神经模型是否在从数据中学习时获得此能力是一个悬而未决的问题。在本文中，我们从形式语言的角度看一下这个问题。我们使用确定性有限状态传感器来制作具有控制组合性的可控属性的无限数量数据集。通过对许多传感器进行随机采样，我们探讨了它们的哪些属性（状态数，字母大小，过渡次数等）有助于通过神经网络的组成关系可学习。通常，我们发现模型要么完全学习关系。关键是过渡覆盖范围，以每个过渡为400个示例设置软可学习性限制。

通用形态（UNIMORPH）项目是一项合作的努力，可为数百种世界语言实例化覆盖范围的标准化形态拐角。该项目包括两个主要的推力：一种无独立的特征架构，用于丰富的形态注释，并以各种语言意识到该模式的各种语言的带注释数据的类型级别资源。本文介绍了过去几年对几个方面的扩张和改进（自McCarthy等人（2020年）以来）。众多语言学家的合作努力增加了67种新语言，其中包括30种濒危语言。我们已经对提取管道进行了一些改进，以解决一些问题，例如缺少性别和马克龙信息。我们还修改了模式，使用了形态学现象所需的层次结构，例如多肢体协议和案例堆叠，同时添加了一些缺失的形态特征，以使模式更具包容性。鉴于上一个UniMorph版本，我们还通过16种语言的词素分割增强了数据库。最后，这个新版本通过通过代表来自metphynet的派生过程的实例丰富数据和注释模式来推动将衍生物形态纳入UniMorph中。

道德是人类最长的智力努力之一。近年来，AI和NLP的领域试图撰写与学习系统的与人类相互作用的学习系统，应该被限制为行为道德。该静脉中的一个提议是建立道德模型，可以采取任意文本，并输出关于所描述的情况的道德判断。在这项工作中，我们专注于对最近提出的Delphi模型的单一案例研究，并为该项目的建议自动化道德判决提供了批评。通过对Delphi的审计，我们检查更广泛的问题，适用于任何类似的尝试。我们讨论了机器道德如何通过专注于技术的当前和近期使用技术的方式来讨论机器伦理，以透明度，民主价值观，并允许直接的责任。

Periocular refers to the region of the face that surrounds the eye socket. This is a feature-rich area that can be used by itself to determine the identity of an individual. It is especially useful when the iris or the face cannot be reliably acquired. This can be the case of unconstrained or uncooperative scenarios, where the face may appear partially occluded, or the subject-to-camera distance may be high. However, it has received revived attention during the pandemic due to masked faces, leaving the ocular region as the only visible facial area, even in controlled scenarios. This paper discusses the state-of-the-art of periocular biometrics, giving an overall framework of its most significant research aspects.

Robustness of different pattern recognition methods is one of the key challenges in autonomous driving, especially when driving in the high variety of road environments and weather conditions, such as gravel roads and snowfall. Although one can collect data from these adverse conditions using cars equipped with sensors, it is quite tedious to annotate the data for training. In this work, we address this limitation and propose a CNN-based method that can leverage the steering wheel angle information to improve the road area semantic segmentation. As the steering wheel angle data can be easily acquired with the associated images, one could improve the accuracy of road area semantic segmentation by collecting data in new road environments without manual data annotation. We demonstrate the effectiveness of the proposed approach on two challenging data sets for autonomous driving and show that when the steering task is used in our segmentation model training, it leads to a 0.1-2.9% gain in the road area mIoU (mean Intersection over Union) compared to the corresponding reference transfer learning model.

Recent work has reported that AI classifiers trained on audio recordings can accurately predict severe acute respiratory syndrome coronavirus 2 (SARSCoV2) infection status. Here, we undertake a large scale study of audio-based deep learning classifiers, as part of the UK governments pandemic response. We collect and analyse a dataset of audio recordings from 67,842 individuals with linked metadata, including reverse transcription polymerase chain reaction (PCR) test outcomes, of whom 23,514 tested positive for SARS CoV 2. Subjects were recruited via the UK governments National Health Service Test-and-Trace programme and the REal-time Assessment of Community Transmission (REACT) randomised surveillance survey. In an unadjusted analysis of our dataset AI classifiers predict SARS-CoV-2 infection status with high accuracy (Receiver Operating Characteristic Area Under the Curve (ROCAUC) 0.846 [0.838, 0.854]) consistent with the findings of previous studies. However, after matching on measured confounders, such as age, gender, and self reported symptoms, our classifiers performance is much weaker (ROC-AUC 0.619 [0.594, 0.644]). Upon quantifying the utility of audio based classifiers in practical settings, we find them to be outperformed by simple predictive scores based on user reported symptoms.

The UK COVID-19 Vocal Audio Dataset is designed for the training and evaluation of machine learning models that classify SARS-CoV-2 infection status or associated respiratory symptoms using vocal audio. The UK Health Security Agency recruited voluntary participants through the national Test and Trace programme and the REACT-1 survey in England from March 2021 to March 2022, during dominant transmission of the Alpha and Delta SARS-CoV-2 variants and some Omicron variant sublineages. Audio recordings of volitional coughs, exhalations, and speech were collected in the 'Speak up to help beat coronavirus' digital survey alongside demographic, self-reported symptom and respiratory condition data, and linked to SARS-CoV-2 test results. The UK COVID-19 Vocal Audio Dataset represents the largest collection of SARS-CoV-2 PCR-referenced audio recordings to date. PCR results were linked to 70,794 of 72,999 participants and 24,155 of 25,776 positive cases. Respiratory symptoms were reported by 45.62% of participants. This dataset has additional potential uses for bioacoustics research, with 11.30% participants reporting asthma, and 27.20% with linked influenza PCR test results.