SWIM: A simple word interaction model for implicit discourse relation recognition
Lei, W Wang, X Liu, M Ilievski, I He, X Kan, MY
Wang, X
Liu, M
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Abstract
Capturing the semantic interaction of pairs of words across arguments and proper argument representation are both crucial issues in implicit discourse relation recognition. The current state-of-the-art represents arguments as distributional vectors that are computed via bi-directional Long Short-Term Memory networks (BiLSTMs), known to have significant model complexity. In contrast, we demonstrate that word-weighted averaging can encode argument representation which can be incorporated with word pair information efficiently. By saving an order of magnitude in parameters and eschewing the recurrent structure, our proposed model achieves equivalent performance, but trains seven times faster.
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Source Title
Twenty-Sixth International Joint Conference on Artificial Intelligence
Publisher
International Joint Conferences on Artificial Intelligence Organization
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Date
2017-08
DOI
10.24963/ijcai.2017/562
Type
Conference Paper