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    <abstract>We argue that external commonsense knowledge and linguistic constraints need to be incorporated into neural network models for mitigating data sparsity issues and further improving the performance of discourse parsing. Realizing that external knowledge and linguistic constraints may not always apply in understanding a particular context, we propose a regularization approach that tightly integrates these constraints with contexts for deriving word representations. Meanwhile, it balances attentions over contexts and constraints through adding a regularization term into the objective function. Experiments show that our knowledge regularization approach outperforms all previous systems on the benchmark dataset PDTB for discourse parsing.</abstract>
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%0 Conference Proceedings
%T A Regularization Approach for Incorporating Event Knowledge and Coreference Relations into Neural Discourse Parsing
%A Dai, Zeyu
%A Huang, Ruihong
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F dai-huang-2019-regularization
%X We argue that external commonsense knowledge and linguistic constraints need to be incorporated into neural network models for mitigating data sparsity issues and further improving the performance of discourse parsing. Realizing that external knowledge and linguistic constraints may not always apply in understanding a particular context, we propose a regularization approach that tightly integrates these constraints with contexts for deriving word representations. Meanwhile, it balances attentions over contexts and constraints through adding a regularization term into the objective function. Experiments show that our knowledge regularization approach outperforms all previous systems on the benchmark dataset PDTB for discourse parsing.
%R 10.18653/v1/D19-1295
%U https://aclanthology.org/D19-1295
%U https://doi.org/10.18653/v1/D19-1295
%P 2976-2987

Markdown (Informal)

[A Regularization Approach for Incorporating Event Knowledge and Coreference Relations into Neural Discourse Parsing](https://aclanthology.org/D19-1295) (Dai & Huang, EMNLP-IJCNLP 2019)

• A Regularization Approach for Incorporating Event Knowledge and Coreference Relations into Neural Discourse Parsing (Dai & Huang, EMNLP-IJCNLP 2019)

ACL

• Zeyu Dai and Ruihong Huang. 2019. A Regularization Approach for Incorporating Event Knowledge and Coreference Relations into Neural Discourse Parsing. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 2976–2987, Hong Kong, China. Association for Computational Linguistics.