Data Augmentation for BERT Fine-Tuning in Open-Domain Question Answering

14 Apr 2019Wei YangYuqing XieLuchen TanKun XiongMing LiJimmy Lin

Recently, a simple combination of passage retrieval using off-the-shelf IR techniques and a BERT reader was found to be very effective for question answering directly on Wikipedia, yielding a large improvement over the previous state of the art on a standard benchmark dataset. In this paper, we present a data augmentation technique using distant supervision that exploits positive as well as negative examples... (read more)

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TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Open-Domain Question Answering SQuAD1.1 dev BERTserini EM 50.2 # 1

Methods used in the Paper