@inproceedings{tay-etal-2019-simple,
title = "Simple and Effective Curriculum Pointer-Generator Networks for Reading Comprehension over Long Narratives",
author = "Tay, Yi and
Wang, Shuohang and
Luu, Anh Tuan and
Fu, Jie and
Phan, Minh C. and
Yuan, Xingdi and
Rao, Jinfeng and
Hui, Siu Cheung and
Zhang, Aston",
editor = "Korhonen, Anna and
Traum, David and
M{\`a}rquez, Llu{\'\i}s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1486",
doi = "10.18653/v1/P19-1486",
pages = "4922--4931",
abstract = "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.",
}
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Simple and Effective Curriculum Pointer-Generator Networks for Reading Comprehension over Long Narratives
%A Tay, Yi
%A Wang, Shuohang
%A Luu, Anh Tuan
%A Fu, Jie
%A Phan, Minh C.
%A Yuan, Xingdi
%A Rao, Jinfeng
%A Hui, Siu Cheung
%A Zhang, Aston
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F tay-etal-2019-simple
%X 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.
%R 10.18653/v1/P19-1486
%U https://aclanthology.org/P19-1486
%U https://doi.org/10.18653/v1/P19-1486
%P 4922-4931
Markdown (Informal)
[Simple and Effective Curriculum Pointer-Generator Networks for Reading Comprehension over Long Narratives](https://aclanthology.org/P19-1486) (Tay et al., ACL 2019)
ACL
- Yi Tay, Shuohang Wang, Anh Tuan Luu, Jie Fu, Minh C. Phan, Xingdi Yuan, Jinfeng Rao, Siu Cheung Hui, and Aston Zhang. 2019. Simple and Effective Curriculum Pointer-Generator Networks for Reading Comprehension over Long Narratives. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4922–4931, Florence, Italy. Association for Computational Linguistics.