Self-learning improvement by means of cloud computing

This paper describes some results of authors' research in machine reading at scale as a support for self-learning, which combines the challenges of document retrieval (finding the relevant articles) with that of machine comprehension of text (identifying the answer spans from those articles). Our approach combines a search component based on bigram hashing and TF-IDF (term frequency–inverse document frequency) matching with a multi-layer recurrent neural network model trained to detect answers in Wikipedia paragraphs.