We present 3 different question-answering models trained on the SQuAD2.0 dataset -- BIDAF, DocumentQA and ALBERT Retro-Reader -- demonstrating the improvement of language models in the past three years. Through our research in fine-tuning pre-trained models for question-answering, we developed a novel approach capable of achieving a 2% point improvement in SQuAD2.0 F1 in reduced training time. Our method of re-initializing select layers of a parameter-shared language model is simple yet empirically powerful.
翻译:我们提出了三种不同的问答模式 -- -- BIDAF、DocumentQADA和ALBERT Retro-Reader -- -- 以SQUAD2.0数据集为培训对象的解答模式 -- -- 展示了过去三年语言模式的改进。我们通过对预先培训的解答模式进行微调的研究,开发了一种新颖的方法,能够在减少培训时间的情况下将SQAD2.0 F1提高2%。我们重新启用共享参数语言模式的选定层次的方法简单而有经验力。