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Open domain natural language question answering (QA) is a process of automatically finding answers to questions searching collections of text files. Question answering (QA) is a long-standing challenge in NLP, and the community has introduced several paradigms and datasets for the task over the past few years. These patterns differ from each other in the type of questions and answers and the size of the training data, from a few hundreds to millions of examples. Context-aware QA paradigm and two most notable types of supervisions are coarse sentence-level and fine-grained span-level. In this paper we analyse different intensive researches in semi-supervised learning for question-answering.

SSL-QA: Analysis of Semi-Supervised Learning for QuestionAnswering