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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17) |
| Editors | Carles Sierra |
| Publisher | International Joint Conferences on Artificial Intelligence |
| Pages | 4026-4032 |
| ISBN (Print) | 9780999241103 |
| DOIs | |
| Publication status | Published - Aug 2017 |
| Event | 26th International Joint Conference on Artificial Intelligence (IJCAI 2017) - Melbourne, Australia Duration: 19 Aug 2017 → 25 Aug 2017 https://www.ijcai.org/proceedings/2017/ |
Conference
| Conference | 26th International Joint Conference on Artificial Intelligence (IJCAI 2017) |
|---|---|
| Place | Australia |
| City | Melbourne |
| Period | 19/08/17 → 25/08/17 |
| Internet address |
Bibliographical note
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