Article comprehension is an important challenge in natural language processing with many applications such as article generation or image-to-article retrieval. Prior work typically encodes all tokens in articles uniformly using pretrained language models. However, in many applications, such as understanding news stories, these articles are based on real-world events and may reference many named entities that are difficult to accurately recognize and predict by language models. To address this challenge, we propose an ENtity-aware article GeneratIoN and rEtrieval (ENGINE) framework, to explicitly incorporate named entities into language models. ENGINE has two main components: a named-entity extraction module to extract named entities from both metadata and embedded images associated with articles, and an entity-aware mechanism that enhances the model's ability to recognize and predict entity names. We conducted experiments on three public datasets: GoodNews, VisualNews, and WikiText, where our results demonstrate that our model can boost both article generation and article retrieval performance, with a 4-5 perplexity improvement in article generation and a 3-4% boost in recall@1 in article retrieval. We release our implementation at https://github.com/Zhongping-Zhang/ENGINE .

Meteorologist Mike Slifer said to expect another chilly start on Tuesday. Then, he tracked possible rain for Thursday. Five children, all from Derby, CT, were killed in a car crash in Scarsdale, NY on Sunday morning. Meteorologist Mike Slifer said the next couple of days would be sunny. Clouds start to thicken on Wednesday.

Pharmaceutical companies spend a lot of time testing potential drugs, and they end up wasting much of that effort on candidates that don't pan out. Kyle Swanson wants to change that. A master's student in computer science and engineering, Swanson is working on a project that involves feeding a computer information about chemical compounds that have or have not worked as drugs in the past. From this input, the machine "learns" to predict which kinds of new compounds have the most promise as drug candidates, potentially saving money and time otherwise spent on testing. Several prominent companies have already adopted the software as their new model.

Nearly everyone has had an experience that exposes just how dependent we have become on artificial intelligence (AI). It often comes in the back seat of a car. That's where I was a few months ago, sitting in a rideshare from suburban Scarsdale, New York, to New York City. The driver had recently emigrated from Nepal. Once, he would have had to invest a lot of time into learning the area well before he could transport a passenger.