@inproceedings{lan-etal-2017-multi,
title = "Multi-task Attention-based Neural Networks for Implicit Discourse Relationship Representation and Identification",
author = "Lan, Man and
Wang, Jianxiang and
Wu, Yuanbin and
Niu, Zheng-Yu and
Wang, Haifeng",
editor = "Palmer, Martha and
Hwa, Rebecca and
Riedel, Sebastian",
booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D17-1134",
doi = "10.18653/v1/D17-1134",
pages = "1299--1308",
abstract = "We present a novel multi-task attention based neural network model to address implicit discourse relationship representation and identification through two types of representation learning, an attention based neural network for learning discourse relationship representation with two arguments and a multi-task framework for learning knowledge from annotated and unannotated corpora. The extensive experiments have been performed on two benchmark corpora (i.e., PDTB and CoNLL-2016 datasets). Experimental results show that our proposed model outperforms the state-of-the-art systems on benchmark corpora.",
}
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<abstract>We present a novel multi-task attention based neural network model to address implicit discourse relationship representation and identification through two types of representation learning, an attention based neural network for learning discourse relationship representation with two arguments and a multi-task framework for learning knowledge from annotated and unannotated corpora. The extensive experiments have been performed on two benchmark corpora (i.e., PDTB and CoNLL-2016 datasets). Experimental results show that our proposed model outperforms the state-of-the-art systems on benchmark corpora.</abstract>
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%0 Conference Proceedings
%T Multi-task Attention-based Neural Networks for Implicit Discourse Relationship Representation and Identification
%A Lan, Man
%A Wang, Jianxiang
%A Wu, Yuanbin
%A Niu, Zheng-Yu
%A Wang, Haifeng
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F lan-etal-2017-multi
%X We present a novel multi-task attention based neural network model to address implicit discourse relationship representation and identification through two types of representation learning, an attention based neural network for learning discourse relationship representation with two arguments and a multi-task framework for learning knowledge from annotated and unannotated corpora. The extensive experiments have been performed on two benchmark corpora (i.e., PDTB and CoNLL-2016 datasets). Experimental results show that our proposed model outperforms the state-of-the-art systems on benchmark corpora.
%R 10.18653/v1/D17-1134
%U https://aclanthology.org/D17-1134
%U https://doi.org/10.18653/v1/D17-1134
%P 1299-1308
Markdown (Informal)
[Multi-task Attention-based Neural Networks for Implicit Discourse Relationship Representation and Identification](https://aclanthology.org/D17-1134) (Lan et al., EMNLP 2017)
ACL