@inproceedings{wang-etal-2018-multi,
title = "Multi-Perspective Context Aggregation for Semi-supervised Cloze-style Reading Comprehension",
author = "Wang, Liang and
Li, Sujian and
Zhao, Wei and
Shen, Kewei and
Sun, Meng and
Jia, Ruoyu and
Liu, Jingming",
editor = "Bender, Emily M. and
Derczynski, Leon and
Isabelle, Pierre",
booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
month = aug,
year = "2018",
address = "Santa Fe, New Mexico, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/C18-1073",
pages = "857--867",
abstract = "Cloze-style reading comprehension has been a popular task for measuring the progress of natural language understanding in recent years. In this paper, we design a novel multi-perspective framework, which can be seen as the joint training of heterogeneous experts and aggregate context information from different perspectives. Each perspective is modeled by a simple aggregation module. The outputs of multiple aggregation modules are fed into a one-timestep pointer network to get the final answer. At the same time, to tackle the problem of insufficient labeled data, we propose an efficient sampling mechanism to automatically generate more training examples by matching the distribution of candidates between labeled and unlabeled data. We conduct our experiments on a recently released cloze-test dataset CLOTH (Xie et al., 2017), which consists of nearly 100k questions designed by professional teachers. Results show that our method achieves new state-of-the-art performance over previous strong baselines.",
}
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<abstract>Cloze-style reading comprehension has been a popular task for measuring the progress of natural language understanding in recent years. In this paper, we design a novel multi-perspective framework, which can be seen as the joint training of heterogeneous experts and aggregate context information from different perspectives. Each perspective is modeled by a simple aggregation module. The outputs of multiple aggregation modules are fed into a one-timestep pointer network to get the final answer. At the same time, to tackle the problem of insufficient labeled data, we propose an efficient sampling mechanism to automatically generate more training examples by matching the distribution of candidates between labeled and unlabeled data. We conduct our experiments on a recently released cloze-test dataset CLOTH (Xie et al., 2017), which consists of nearly 100k questions designed by professional teachers. Results show that our method achieves new state-of-the-art performance over previous strong baselines.</abstract>
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%0 Conference Proceedings
%T Multi-Perspective Context Aggregation for Semi-supervised Cloze-style Reading Comprehension
%A Wang, Liang
%A Li, Sujian
%A Zhao, Wei
%A Shen, Kewei
%A Sun, Meng
%A Jia, Ruoyu
%A Liu, Jingming
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F wang-etal-2018-multi
%X Cloze-style reading comprehension has been a popular task for measuring the progress of natural language understanding in recent years. In this paper, we design a novel multi-perspective framework, which can be seen as the joint training of heterogeneous experts and aggregate context information from different perspectives. Each perspective is modeled by a simple aggregation module. The outputs of multiple aggregation modules are fed into a one-timestep pointer network to get the final answer. At the same time, to tackle the problem of insufficient labeled data, we propose an efficient sampling mechanism to automatically generate more training examples by matching the distribution of candidates between labeled and unlabeled data. We conduct our experiments on a recently released cloze-test dataset CLOTH (Xie et al., 2017), which consists of nearly 100k questions designed by professional teachers. Results show that our method achieves new state-of-the-art performance over previous strong baselines.
%U https://aclanthology.org/C18-1073
%P 857-867
Markdown (Informal)
[Multi-Perspective Context Aggregation for Semi-supervised Cloze-style Reading Comprehension](https://aclanthology.org/C18-1073) (Wang et al., COLING 2018)
ACL
- Liang Wang, Sujian Li, Wei Zhao, Kewei Shen, Meng Sun, Ruoyu Jia, and Jingming Liu. 2018. Multi-Perspective Context Aggregation for Semi-supervised Cloze-style Reading Comprehension. In Proceedings of the 27th International Conference on Computational Linguistics, pages 857–867, Santa Fe, New Mexico, USA. Association for Computational Linguistics.