Machine Learning, NLP, Computer Vision, Information Retrieval, Transformers.
that contexts are concatenated as input to the reader model and the question can attend to all documents simultaneously to generate the answer. The work results in state-of-the-art for the first retrieval stage and also for “end-to-end” QA in Natural Questions and TriviaQA datasets. Retriever-nly