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Transformer-based pre-trained models, such as BERT, have achieved remarkableresults on machine reading comprehension. However, due to the constraint ofencoding length (e.g., 512 WordPiece tokens), a long document is usually splitinto multiple chunks that are independently read. It results in the readingfield being limited to individual chunks without information collaboration forlong document machine reading comprehension. To address this problem, wepropose RoR, a read-over-read method, which expands the reading field fromchunk to document. Specifically, RoR includes a chunk reader and a documentreader. The former first predicts a set of regional answers for each chunk,which are then compacted into a highly-condensed version of the originaldocument, guaranteeing to be encoded once. The latter further predicts theglobal answers from this condensed document. Eventually, a voting strategy isutilized to aggregate and rerank the regional and global answers for finalprediction. Extensive experiments on two benchmarks QuAC and TriviaQAdemonstrate the effectiveness of RoR for long document reading. Notably, RoRranks 1st place on the QuAC leaderboard (https://quac.ai/) at the time ofsubmission (May 17th, 2021).

RoR: Read-over-Read for Long Document Machine Reading Comprehension