TegFormer: Topic-to-Essay Generation with Good Topic Coverage and High Text Coherence

Creating an essay based on a few given topics is a challenging NLP task. Although several effective methods for this problem, topic-to-essay generation, have appeared recently, there is still much room for improvement, especially in terms of the coverage of the given topics and the coherence of the generated text. In this paper, we propose a novel approach called TegFormer which utilizes the Transformer architecture where the encoder is enriched with domain-specific contexts while the decoder is enhanced by a large-scale pre-trained language model. Specifically, a \emph{Topic-Extension} layer capturing the interaction between the given topics and their domain-specific contexts is plugged into the encoder. Since the given topics are usually concise and sparse, such an additional layer can bring more topic-related semantics in to facilitate the subsequent natural language generation. Moreover, an \emph{Embedding-Fusion} module that combines the domain-specific word embeddings learnt from the given corpus and the general-purpose word embeddings provided by a GPT-2 model pre-trained on massive text data is integrated into the decoder. Since GPT-2 is at a much larger scale, it contains a lot more implicit linguistic knowledge which would help the decoder to produce more grammatical and readable text. Extensive experiments have shown that the pieces of text generated by TegFormer have better topic coverage and higher text coherence than those from SOTA topic-to-essay techniques, according to automatic and human evaluations. As revealed by ablation studies, both the Topic-Extension layer and the Embedding-Fusion module contribute substantially to TegFormer's performance advantage

Robust and Explainable Identification of Logical Fallacies in Natural Language Arguments

The spread of misinformation, propaganda, and flawed argumentation has been amplified in the Internet era. Given the volume of data and the subtlety of identifying violations of argumentation norms, supporting information analytics tasks, like content moderation, with trustworthy methods that can identify logical fallacies is essential. In this paper, we formalize prior theoretical work on logical fallacies into a comprehensive three-stage evaluation framework of detection, coarse-grained, and fine-grained classification. We adapt existing evaluation datasets for each stage of the evaluation. We employ three families of robust and explainable methods based on prototype reasoning, instance-based reasoning, and knowledge injection. The methods combine language models with background knowledge and explainable mechanisms. Moreover, we address data sparsity with strategies for data augmentation and curriculum learning. Our three-stage framework natively consolidates prior datasets and methods from existing tasks, like propaganda detection, serving as an overarching evaluation testbed. We extensively evaluate these methods on our datasets, focusing on their robustness and explainability. Our results provide insight into the strengths and weaknesses of the methods on different components and fallacy classes, indicating that fallacy identification is a challenging task that may require specialized forms of reasoning to capture various classes. We share our open-source code and data on GitHub to support further work on logical fallacy identification

Religious symbolism and the experience of life as meaningful: addition, enhancement, or both?

This paper explores the question of how religious symbolism functions to provide a more meaningful or enriched experience of life. It examines a common and highly influential view, referred to here as the “source model”, for which this function requires the addition to experience of transcendent meanings generated by rituals and other specially adapted kinds of symbolic activity. Using Robert Bellah’s Religion in Human Evolution and Clifford Geertz’s “Religion as a Cultural System” as representative examples, I critique a key premise of the source model, namely that the meaning-making function of religious symbolism evolved in response to a universal experience of life as problematic. I argue that the experience of life as problematic is a product of symbolism, not a precondition. Moreover, with respect to this experience, I propose that symbolism functions not to add meaning but to enhance meanings that are vaguely discerned in everyday life. I close with the suggestion that an enhanced experience of life as problematic is itself a kind of enriched meaning and an important source of the affective power of religious practice