In this paper we present our contribution to the TSAR-2022 Shared Task on Lexical Simplification of the EMNLP 2022 Workshop on Text Simplification, Accessibility, and Readability. Our approach builds on and extends the unsupervised lexical simplification system with pretrained encoders (LSBert) system in the following ways: For the subtask of simplification candidate selection, it utilizes a RoBERTa transformer language model and expands the size of the generated candidate list. For subsequent substitution ranking, it introduces a new feature weighting scheme and adopts a candidate filtering method based on textual entailment to maximize semantic similarity between the target word and its simplification. Our best-performing system improves LSBert by 5.9% accuracy and achieves second place out of 33 ranked solutions.

State-of-the-art text simplification (TS) systems adopt end-to-end neural network models to directly generate the simplified version of the input text, and usually function as a blackbox. Moreover, TS is usually treated as an all-purpose generic task under the assumption of homogeneity, where the same simplification is suitable for all. In recent years, however, there has been increasing recognition of the need to adapt the simplification techniques to the specific needs of different target groups. In this work, we aim to advance current research on explainable and controllable TS in two ways: First, building on recently proposed work to increase the transparency of TS systems, we use a large set of (psycho-)linguistic features in combination with pre-trained language models to improve explainable complexity prediction. Second, based on the results of this preliminary task, we extend a state-of-the-art Seq2Seq TS model, ACCESS, to enable explicit control of ten attributes. The results of experiments show (1) that our approach improves the performance of state-of-the-art models for predicting explainable complexity and (2) that explicitly conditioning the Seq2Seq model on ten attributes leads to a significant improvement in performance in both within-domain and out-of-domain settings.

In recent years, there has been a surge of interest in research on automatic mental health detection (MHD) from social media data leveraging advances in natural language processing and machine learning techniques. While significant progress has been achieved in this interdisciplinary research area, the vast majority of work has treated MHD as a binary classification task. The multiclass classification setup is, however, essential if we are to uncover the subtle differences among the statistical patterns of language use associated with particular mental health conditions. Here, we report on experiments aimed at predicting six conditions (anxiety, attention deficit hyperactivity disorder, bipolar disorder, post-traumatic stress disorder, depression, and psychological stress) from Reddit social media posts. We explore and compare the performance of hybrid and ensemble models leveraging transformer-based architectures (BERT and RoBERTa) and BiLSTM neural networks trained on within-text distributions of a diverse set of linguistic features. This set encompasses measures of syntactic complexity, lexical sophistication and diversity, readability, and register-specific ngram frequencies, as well as sentiment and emotion lexicons. In addition, we conduct feature ablation experiments to investigate which types of features are most indicative of particular mental health conditions.

In recent years, there has been increased interest in building predictive models that harness natural language processing and machine learning techniques to detect emotions from various text sources, including social media posts, micro-blogs or news articles. Yet, deployment of such models in real-world sentiment and emotion applications faces challenges, in particular poor out-of-domain generalizability. This is likely due to domain-specific differences (e.g., topics, communicative goals, and annotation schemes) that make transfer between different models of emotion recognition difficult. In this work we propose approaches for text-based emotion detection that leverage transformer models (BERT and RoBERTa) in combination with Bidirectional Long Short-Term Memory (BiLSTM) networks trained on a comprehensive set of psycholinguistic features. First, we evaluate the performance of our models within-domain on two benchmark datasets: GoEmotion and ISEAR. Second, we conduct transfer learning experiments on six datasets from the Unified Emotion Dataset to evaluate their out-of-domain robustness. We find that the proposed hybrid models improve the ability to generalize to out-of-distribution data compared to a standard transformer-based approach. Moreover, we observe that these models perform competitively on in-domain data.

其中一个关键的交际能力是能够维持单声道言论的流利程度以及产生复杂语言的能力，以令人信服地争论一个立场。在本文中，我们的目标是预测由110名个人7小时的演讲组成的争论演讲的众群数据集中的TED谈话风格的情感评级。通过与三个辩论主题有关的任务提示引发语音样本。该样本总共有2211名来自737名有关的人类评估者，其有关的14个情感类别。我们通过微调预先调整在TED谈判公开演讲的大型数据集上进行了微调的模型来提出有效的方法来预测这些类别的分类任务。我们使用从最先进的自动语音识别系统中获得的流利功能的组合和从自动文本分析系统获得的大量人类解释的语言特征。所有14家评级类别的分类准确度大于60％，评分类别“信息性”的峰值性能为72％。在二次实验中，我们确定了使用SP-in-in-infly群体的特征的相对重要性。

我们提出了一种用于构建线性时间不变（LTI）模型的新颖框架，用于一类稳定的非线性动态的Koopman运算符的数据驱动表示。 Koopman操作员（发电机）将有限维非线性系统升压到可能无限的线性特征空间。为了利用它来建模，需要发现Koopman运算符的有限维表示。学习合适的功能是具有挑战性的，因为一种需要学习koopman-invariant（在动态下线性演变的LTI功能以及相关（跨越原始状态） - 一般无监督的学习任务。对于这个问题的理论上是良好的解决方案，我们通过用潜伏的线性模型的提升的聚集体系来组合扩散综合学习者来提出学习Koopman-Invoriant坐标。使用稳定矩阵的无约束参数化以及上述特征结构，我们学习Koopman操作员特征而不假设预定义的功能库或了解频谱，同时确保操作员近似精度而确保稳定性。我们展示了所提出的方法与众所周知的LASA手写数据集上的最先进方法的卓越效果。