In the absence of readily available labeled data for a given task and language, annotation projection has been proposed as one of the possible strategies to automatically generate annotated data which may then be used to train supervised systems. Annotation projection has often been formulated as the task of projecting, on parallel corpora, some labels from a source into a target language. In this paper we present T-Projection, a new approach for annotation projection that leverages large pretrained text2text language models and state-of-the-art machine translation technology. T-Projection decomposes the label projection task into two subtasks: (i) The candidate generation step, in which a set of projection candidates using a multilingual T5 model is generated and, (ii) the candidate selection step, in which the candidates are ranked based on translation probabilities. We evaluate our method in three downstream tasks and five different languages. Our results show that T-projection improves the average F1 score of previous methods by more than 8 points.

In this paper we present a simple re-ranking method for Automatic Sentence Simplification based on the noisy channel scheme. Instead of directly computing the best simplification given a complex text, the re-ranking method also considers the probability of the simple sentence to produce the complex counterpart, as well as the probability of the simple text itself, according to a language model. Our experiments show that combining these scores outperform the original system in three different English datasets, yielding the best known result in one of them. Adopting the noisy channel scheme opens new ways to infuse additional information into ATS systems, and thus to control important aspects of them, a known limitation of end-to-end neural seq2seq generative models.

分类任务中的预测不确定性通常是模型不足或培训数据不足的结果。在流行的应用程序（例如图像处理）中，通常要求我们通过将它们归因于输入功能来审查这些不确定性。这有助于改善可解释性评估。但是，为此目的，几乎没有有效的框架。香草形式的流行方法用于提供显着性面膜的流行方法，例如塑造或综合梯度，无法适应不确定性的目标。因此，最新的工具相反，通过创建反事实或对抗特征向量来进行，并通过直接比较与原始图像分配属性。在本文中，我们提出了一个新颖的框架，该框架结合了路径积分，反事实解释和生成模型，以获取很少有可观察的人工制品或噪声的归因。我们证明，通过使用流行的基准测定方法和不同复杂性数据集的定量评估，这表现优于现有的替代方案。