Word Sense Disambiguation (WSD) is an NLP task aimed at determining the correct sense of a word in a sentence from discrete sense choices. Although current systems have attained unprecedented performances for such tasks, the nonuniform distribution of word senses during training generally results in systems performing poorly on rare senses. To this end, we consider data augmentation to increase the frequency of these least frequent senses (LFS) to reduce the distributional bias of senses during training. We propose Sense-Maintained Sentence Mixup (SMSMix), a novel word-level mixup method that maintains the sense of a target word. SMSMix smoothly blends two sentences using mask prediction while preserving the relevant span determined by saliency scores to maintain a specific word's sense. To the best of our knowledge, this is the first attempt to apply mixup in NLP while preserving the meaning of a specific word. With extensive experiments, we validate that our augmentation method can effectively give more information about rare senses during training with maintained target sense label.

视听目标语音提取旨在通过查看唇部运动来从嘈杂的混合物中提取某个说话者的语音，这取得了重大进展，结合了时间域的语音分离模型和视觉特征提取器（CNN）。融合音频和视频信息的一个问题是它们具有不同的时间分辨率。当前的大多数研究都会沿时间维度进行视觉特征，以便音频和视频功能能够随时间对齐。但是，我们认为唇部运动主要包含长期或电话级信息。基于这个假设，我们提出了一种融合视听功能的新方法。我们观察到，对于dprnn \ cite {dprnn}，互联维度的时间分辨率可能非常接近视频帧的时间分辨率。像\ cite {sepformer}一样，dprnn中的LSTM被内部内部和牙间的自我注意力所取代，但是在提出的算法中，界界的注意力将视觉特征作为附加特征流。这样可以防止视觉提示的提高采样，从而导致更有效的视听融合。结果表明，与其他基于时间域的视听融合模型相比，我们获得了优越的结果。

语音中的自我监督学习涉及在大规模的未注释的语音语料库上训练语音表示网络，然后将学习的表示形式应用于下游任务。由于语音中SSL学习的大多数下游任务主要集中在语音中的内容信息上，因此最理想的语音表示形式应该能够将不需要的变化（例如说话者的变化）从内容中删除。但是，解开扬声器非常具有挑战性，因为删除说话者的信息也很容易导致内容丢失，而后者的损害通常远远超过了前者的好处。在本文中，我们提出了一种新的SSL方法，该方法可以实现扬声器分解而不会严重丢失内容。我们的方法是根据休伯特框架改编的，并结合了解开机制，以使教师标签和博学的代表规范化。我们在一组与内容相关的下游任务上评估了说话者分解的好处，并观察到我们的扬声器示词表示的一致且著名的性能优势。

无监督的文本到语音综合（TTS）系统学会通过观察以下语言来生成与任何语言中任何书面句子相对应的语音波形：1）用该语言收集的未转录语音波形的集合； 2）用该语言编写的文本集合，无需访问任何抄录的语音。开发这种系统可以显着提高语言技术对语言的可用性，而无需大量平行的语音和文本数据。本文提出了一个基于对齐模块的无监督的TTS系统，该模块输出了伪文本和另一个使用伪文本进行训练和真实文本进行推理的合成模块。我们的无监督系统可以以七种语言的方式实现与监督系统相当的性能，每种语音约10-20小时。还对文本单元和声码器的效果进行了仔细的研究，以更好地了解哪些因素可能影响无监督的TTS性能。可以在https://cactuswiththoughts.github.io/unsuptts-demo上找到我们的模型生成的样品，可以在https://github.com/lwang114/unsuptts上找到我们的代码。

本文定义了公平的主要成分分析（PCA），从而最大限度地减少不同受保护类的维度减少条件分布之间的最大平均差异（MMD）。MMD的掺入自然导致具有良好统计性质的公平性的精确和易易易诊的数学制剂。我们制定公平PCA，经过MMD限制的公平PCA，作为Stiefel歧管的非凸优化，并使用具有平滑（REPMS; LIU和BOUMAL，2019）的Riemannian精确惩罚方法来解决它。重要的是，我们提供当地的最优性保证，并明确显示每个超参数在实际设置中的理论效果，扩展了先前的结果。基于合成和UCI数据集的实验比较表明，我们的方法优于现有工作的差异，公平，公平和运行时。

Research on automated essay scoring has become increasing important because it serves as a method for evaluating students' written-responses at scale. Scalable methods for scoring written responses are needed as students migrate to online learning environments resulting in the need to evaluate large numbers of written-response assessments. The purpose of this study is to describe and evaluate three active learning methods than can be used to minimize the number of essays that must be scored by human raters while still providing the data needed to train a modern automated essay scoring system. The three active learning methods are the uncertainty-based, the topological-based, and the hybrid method. These three methods were used to select essays included as part of the Automated Student Assessment Prize competition that were then classified using a scoring model that was training with the bidirectional encoder representations from transformer language model. All three active learning methods produced strong results, with the topological-based method producing the most efficient classification. Growth rate accuracy was also evaluated. The active learning methods produced different levels of efficiency under different sample size allocations but, overall, all three methods were highly efficient and produced classifications that were similar to one another.

This paper presents a novel framework for planning in unknown and occluded urban spaces. We specifically focus on turns and intersections where occlusions significantly impact navigability. Our approach uses an inpainting model to fill in a sparse, occluded, semantic lidar point cloud and plans dynamically feasible paths for a vehicle to traverse through the open and inpainted spaces. We demonstrate our approach using a car's lidar data with real-time occlusions, and show that by inpainting occluded areas, we can plan longer paths, with more turn options compared to without inpainting; in addition, our approach more closely follows paths derived from a planner with no occlusions (called the ground truth) compared to other state of the art approaches.

Feature acquisition algorithms address the problem of acquiring informative features while balancing the costs of acquisition to improve the learning performances of ML models. Previous approaches have focused on calculating the expected utility values of features to determine the acquisition sequences. Other approaches formulated the problem as a Markov Decision Process (MDP) and applied reinforcement learning based algorithms. In comparison to previous approaches, we focus on 1) formulating the feature acquisition problem as a MDP and applying Monte Carlo Tree Search, 2) calculating the intermediary rewards for each acquisition step based on model improvements and acquisition costs and 3) simultaneously optimizing model improvement and acquisition costs with multi-objective Monte Carlo Tree Search. With Proximal Policy Optimization and Deep Q-Network algorithms as benchmark, we show the effectiveness of our proposed approach with experimental study.

The celebrated proverb that "speech is silver, silence is golden" has a long multinational history and multiple specific meanings. In written texts punctuation can in fact be considered one of its manifestations. Indeed, the virtue of effectively speaking and writing involves - often decisively - the capacity to apply the properly placed breaks. In the present study, based on a large corpus of world-famous and representative literary texts in seven major Western languages, it is shown that the distribution of intervals between consecutive punctuation marks in almost all texts can universally be characterised by only two parameters of the discrete Weibull distribution which can be given an intuitive interpretation in terms of the so-called hazard function. The values of these two parameters tend to be language-specific, however, and even appear to navigate translations. The properties of the computed hazard functions indicate that among the studied languages, English turns out to be the least constrained by the necessity to place a consecutive punctuation mark to partition a sequence of words. This may suggest that when compared to other studied languages, English is more flexible, in the sense of allowing longer uninterrupted sequences of words. Spanish reveals similar tendency to only a bit lesser extent.

This report summarizes the 3rd International Verification of Neural Networks Competition (VNN-COMP 2022), held as a part of the 5th Workshop on Formal Methods for ML-Enabled Autonomous Systems (FoMLAS), which was collocated with the 34th International Conference on Computer-Aided Verification (CAV). VNN-COMP is held annually to facilitate the fair and objective comparison of state-of-the-art neural network verification tools, encourage the standardization of tool interfaces, and bring together the neural network verification community. To this end, standardized formats for networks (ONNX) and specification (VNN-LIB) were defined, tools were evaluated on equal-cost hardware (using an automatic evaluation pipeline based on AWS instances), and tool parameters were chosen by the participants before the final test sets were made public. In the 2022 iteration, 11 teams participated on a diverse set of 12 scored benchmarks. This report summarizes the rules, benchmarks, participating tools, results, and lessons learned from this iteration of this competition.