多目标梯度方法正在成为解决多目标问题的标准。其中，他们表现出有希望开发具有相关和相互矛盾的目标的多目标推荐系统。经典多梯度〜下降通常依赖于梯度的组合，而不是梯度的第一和第二矩的计算。这导致了脆性行为，并错过了解决方案空间中的重要区域。在这项工作中，我们创建了一种多目标模型 - 不可知的暗示方法，利用ADAM优化器在单目标问题中的优势。这将校正和稳定〜在计算常见的梯度下降载体之前〜〜〜梯度，同时优化所有目标。我们评估多目标的好处对两个多目标推荐系统和三种不同的客观组合，两者都相关或相互矛盾。我们报告了大量改进，用三种不同的Pareto正面指标测量：超凡镜，覆盖范围和间距。最后，我们展示了\ Textit {adamized} Pareto Front严格地占据了前一个目标对的前一个。

使用个性化解释来支持建议，以增加信任和感知质量。但是，为了实际获取更好的建议，需要一种用户通过与解释进行交互来修改推荐标准的手段。我们介绍了一种新颖的技术，使用方面标记，学会从审查文本生成关于建议的个性化解释，并且我们表明人类用户明显更喜欢通过最先进技术产生的解释这些解释。我们的工作最重要的创新是它允许用户通过批评文本解释来对推荐作出反应：删除（对称添加）它们不喜欢的某些方面或不再相关（对称地是感兴趣的）。系统根据批评更新其用户模型和产生的建议。这是基于一种具有文本解释的单一和多步批判的新型无监督批评方法。两个现实世界数据集的实验表明，我们的系统是第一个在适应多步批评中表达的偏好方面实现良好性能的实验。

Numerous works use word embedding-based metrics to quantify societal biases and stereotypes in texts. Recent studies have found that word embeddings can capture semantic similarity but may be affected by word frequency. In this work we study the effect of frequency when measuring female vs. male gender bias with word embedding-based bias quantification methods. We find that Skip-gram with negative sampling and GloVe tend to detect male bias in high frequency words, while GloVe tends to return female bias in low frequency words. We show these behaviors still exist when words are randomly shuffled. This proves that the frequency-based effect observed in unshuffled corpora stems from properties of the metric rather than from word associations. The effect is spurious and problematic since bias metrics should depend exclusively on word co-occurrences and not individual word frequencies. Finally, we compare these results with the ones obtained with an alternative metric based on Pointwise Mutual Information. We find that this metric does not show a clear dependence on frequency, even though it is slightly skewed towards male bias across all frequencies.

This report summarizes the work carried out by the authors during the Twelfth Montreal Industrial Problem Solving Workshop, held at Universit\'e de Montr\'eal in August 2022. The team tackled a problem submitted by CBC/Radio-Canada on the theme of Automatic Text Simplification (ATS).

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.

360-degree panoramic videos have gained considerable attention in recent years due to the rapid development of head-mounted displays (HMDs) and panoramic cameras. One major problem in streaming panoramic videos is that panoramic videos are much larger in size compared to traditional ones. Moreover, the user devices are often in a wireless environment, with limited battery, computation power, and bandwidth. To reduce resource consumption, researchers have proposed ways to predict the users' viewports so that only part of the entire video needs to be transmitted from the server. However, the robustness of such prediction approaches has been overlooked in the literature: it is usually assumed that only a few models, pre-trained on past users' experiences, are applied for prediction to all users. We observe that those pre-trained models can perform poorly for some users because they might have drastically different behaviors from the majority, and the pre-trained models cannot capture the features in unseen videos. In this work, we propose a novel meta learning based viewport prediction paradigm to alleviate the worst prediction performance and ensure the robustness of viewport prediction. This paradigm uses two machine learning models, where the first model predicts the viewing direction, and the second model predicts the minimum video prefetch size that can include the actual viewport. We first train two meta models so that they are sensitive to new training data, and then quickly adapt them to users while they are watching the videos. Evaluation results reveal that the meta models can adapt quickly to each user, and can significantly increase the prediction accuracy, especially for the worst-performing predictions.

This paper presents a corpus annotated for the task of direct-speech extraction in Croatian. The paper focuses on the annotation of the quotation, co-reference resolution, and sentiment annotation in SETimes news corpus in Croatian and on the analysis of its language-specific differences compared to English. From this, a list of the phenomena that require special attention when performing these annotations is derived. The generated corpus with quotation features annotations can be used for multiple tasks in the field of Natural Language Processing.

With the ever-growing popularity of the field of NLP, the demand for datasets in low resourced-languages follows suit. Following a previously established framework, in this paper, we present the UNER dataset, a multilingual and hierarchical parallel corpus annotated for named-entities. We describe in detail the developed procedure necessary to create this type of dataset in any language available on Wikipedia with DBpedia information. The three-step procedure extracts entities from Wikipedia articles, links them to DBpedia, and maps the DBpedia sets of classes to the UNER labels. This is followed by a post-processing procedure that significantly increases the number of identified entities in the final results. The paper concludes with a statistical and qualitative analysis of the resulting dataset.

This article presents the application of the Universal Named Entity framework to generate automatically annotated corpora. By using a workflow that extracts Wikipedia data and meta-data and DBpedia information, we generated an English dataset which is described and evaluated. Furthermore, we conducted a set of experiments to improve the annotations in terms of precision, recall, and F1-measure. The final dataset is available and the established workflow can be applied to any language with existing Wikipedia and DBpedia. As part of future research, we intend to continue improving the annotation process and extend it to other languages.

As machine learning (ML) systems get adopted in more critical areas, it has become increasingly crucial to address the bias that could occur in these systems. Several fairness pre-processing algorithms are available to alleviate implicit biases during model training. These algorithms employ different concepts of fairness, often leading to conflicting strategies with consequential trade-offs between fairness and accuracy. In this work, we evaluate three popular fairness pre-processing algorithms and investigate the potential for combining all algorithms into a more robust pre-processing ensemble. We report on lessons learned that can help practitioners better select fairness algorithms for their models.