August 1, 2019
This paper tackles the problem of reading comprehension over long narratives where documents easily span over thousands of tokens. We propose a curriculum learning (CL) based Pointer-Generator framework for reading/sampling over large documents, enabling diverse training of the neural model based on the notion of alternating contextual difficulty. This can be interpreted as a form of domain randomization and/or generative pretraining during training. To this end, the usage of the Pointer-Generator softens the requirement of having the answer within the context, enabling us to construct diverse training samples for learning. Additionally, we propose a new Introspective Alignment Layer (IAL), which reasons over decomposed alignments using block-based self-attention. We evaluate our proposed method on the NarrativeQA reading comprehension benchmark, achieving state-of-the-art performance, improving existing baselines by 51% relative improvement on BLEU-4 and 17% relative improvement on Rouge-L. Extensive ablations confirm the effectiveness of our proposed IAL and CL components.
August 01, 2019
Yi Tay, Shuohang Wang, Luu Anh Tuan, Jie Fu, Minh C. Phan, Xingdi Yuan, Jinfeng Rao, Siu Cheung Hui, Aston Zhang
August 01, 2019
July 29, 2019
Jiatao Gu, Yong Wang, Kyunghyun Cho, Victor O.K. Li
July 29, 2019
June 11, 2019
Jae Sung Park, Marcus Rohrbach, Trevor Darrell, Anna Rohrbach
June 11, 2019
June 10, 2019
Tianxiao Shen, Myle Ott, Michael Auli, Marc'Aurelio Ranzato
June 10, 2019