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End-to-End Open-Domain Question Answering with BERTserini - 2019

Research Area:  Machine Learning

Abstract:

We demonstrate an end-to-end question answering system that integrates BERT with the open-source Anserini information retrieval toolkit. In contrast to most question answering and reading comprehension models today, which operate over small amounts of input text, our system integrates best practices from IR with a BERT-based reader to identify answers from a large corpus of Wikipedia articles in an end-to-end fashion. We report large improvements over previous results on a standard benchmark test collection, showing that fine-tuning pretrained BERT with SQuAD is sufficient to achieve high accuracy in identifying answer spa

Author(s) Name:  Wei Yang, Yuqing Xie, Aileen Lin, Xingyu Li, Luchen Tan, Kun Xiong, Ming Li, Jimmy Lin

Journal name:  Computer Science

Conferrence name:  

Publisher name:  arXiv:1902.01718

DOI:  10.48550/arXiv.1902.01718

Volume Information: