Comment sections allow users to share their personal experiences, discuss and form different opinions, and build communities out of organic conversations. However, many comment sections present chronological ranking to all users. In this paper, I dis
cuss personalization approaches in comment sections based on different objectives for newsrooms and researchers to consider. I propose algorithmic and interface designs when personalizing the presentation of comments based on different objectives including relevance, diversity, and education/background information. I further explain how transparency, user control, and comment type diversity could help users most benefit from the personalized interacting experience.
Large pretrained language models using the transformer neural network architecture are becoming a dominant methodology for many natural language processing tasks, such as question answering, text classification, word sense disambiguation, text comple
tion and machine translation. Commonly comprising hundreds of millions of parameters, these models offer state-of-the-art performance, but at the expense of interpretability. The attention mechanism is the main component of transformer networks. We present AttViz, a method for exploration of self-attention in transformer networks, which can help in explanation and debugging of the trained models by showing associations between text tokens in an input sequence. We show that existing deep learning pipelines can be explored with AttViz, which offers novel visualizations of the attention heads and their aggregations. We implemented the proposed methods in an online toolkit and an offline library. Using examples from news analysis, we demonstrate how AttViz can be used to inspect and potentially better understand what a model has learned.
