@inproceedings{varia-etal-2019-discourse,
title = "Discourse Relation Prediction: Revisiting Word Pairs with Convolutional Networks",
author = "Varia, Siddharth and
Hidey, Christopher and
Chakrabarty, Tuhin",
editor = "Nakamura, Satoshi and
Gasic, Milica and
Zukerman, Ingrid and
Skantze, Gabriel and
Nakano, Mikio and
Papangelis, Alexandros and
Ultes, Stefan and
Yoshino, Koichiro",
booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
month = sep,
year = "2019",
address = "Stockholm, Sweden",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-5951",
doi = "10.18653/v1/W19-5951",
pages = "442--452",
abstract = "Word pairs across argument spans have been shown to be effective for predicting the discourse relation between them. We propose an approach to distill knowledge from word pairs for discourse relation classification with convolutional neural networks by incorporating joint learning of implicit and explicit relations. Our novel approach of representing the input as word pairs achieves state-of-the-art results on four-way classification of both implicit and explicit relations as well as one of the binary classification tasks. For explicit relation prediction, we achieve around 20{\%} error reduction on the four-way task. At the same time, compared to a two-layered Bi-LSTM-CRF model, our model is able to achieve these results with half the number of learnable parameters and approximately half the amount of training time.",
}
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%0 Conference Proceedings
%T Discourse Relation Prediction: Revisiting Word Pairs with Convolutional Networks
%A Varia, Siddharth
%A Hidey, Christopher
%A Chakrabarty, Tuhin
%Y Nakamura, Satoshi
%Y Gasic, Milica
%Y Zukerman, Ingrid
%Y Skantze, Gabriel
%Y Nakano, Mikio
%Y Papangelis, Alexandros
%Y Ultes, Stefan
%Y Yoshino, Koichiro
%S Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue
%D 2019
%8 September
%I Association for Computational Linguistics
%C Stockholm, Sweden
%F varia-etal-2019-discourse
%X Word pairs across argument spans have been shown to be effective for predicting the discourse relation between them. We propose an approach to distill knowledge from word pairs for discourse relation classification with convolutional neural networks by incorporating joint learning of implicit and explicit relations. Our novel approach of representing the input as word pairs achieves state-of-the-art results on four-way classification of both implicit and explicit relations as well as one of the binary classification tasks. For explicit relation prediction, we achieve around 20% error reduction on the four-way task. At the same time, compared to a two-layered Bi-LSTM-CRF model, our model is able to achieve these results with half the number of learnable parameters and approximately half the amount of training time.
%R 10.18653/v1/W19-5951
%U https://aclanthology.org/W19-5951
%U https://doi.org/10.18653/v1/W19-5951
%P 442-452
Markdown (Informal)
[Discourse Relation Prediction: Revisiting Word Pairs with Convolutional Networks](https://aclanthology.org/W19-5951) (Varia et al., SIGDIAL 2019)
ACL