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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence / EAAI-20

SAM-Net: Integrating Event-Level and Chain-Level Attentions to Predict What Happens Next

February 1, 2023

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Authors

Shangwen Lv

Chinese Academy of Sciences


Wanhui Qian

Chinese Academy of Sciences


Longtao Huang

Chinese Academy of Science


Jizhong Han

Chinese Academy of Sciences


Songlin Hu

Chinese Academy of Sciences


DOI:

10.1609/aaai.v33i01.33016802


Abstract:

Scripts represent knowledge of event sequences that can help text understanding. Script event prediction requires to measure the relation between an existing chain and the subsequent event. The dominant approaches either focus on the effects of individual events, or the influence of the chain sequence. However, only considering individual events will lose much semantic relations within the event chain, and only considering the sequence of the chain will introduce much noise. With our observations, both the individual events and the event segments within the chain can facilitate the prediction of the subsequent event. This paper develops self attention mechanism to focus on diverse event segments within the chain and the event chain is represented as a set of event segments. We utilize the event-level attention to model the relations between subsequent events and individual events. Then, we propose the chain-level attention to model the relations between subsequent events and event segments within the chain. Finally, we integrate event-level and chain-level attentions to interact with the chain to predict what happens next. Comprehensive experiment results on the widely used New York Times corpus demonstrate that our model achieves better results than other state-of-the-art baselines by adopting the evaluation of Multi-Choice Narrative Cloze task.

Topics: AAAI

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HOW TO CITE:

Shangwen Lv||Wanhui Qian||Longtao Huang||Jizhong Han||Songlin Hu SAM-Net: Integrating Event-Level and Chain-Level Attentions to Predict What Happens Next Proceedings of the AAAI Conference on Artificial Intelligence (2019) 6802-6809.

Shangwen Lv||Wanhui Qian||Longtao Huang||Jizhong Han||Songlin Hu SAM-Net: Integrating Event-Level and Chain-Level Attentions to Predict What Happens Next AAAI 2019, 6802-6809.

Shangwen Lv||Wanhui Qian||Longtao Huang||Jizhong Han||Songlin Hu (2019). SAM-Net: Integrating Event-Level and Chain-Level Attentions to Predict What Happens Next. Proceedings of the AAAI Conference on Artificial Intelligence, 6802-6809.

Shangwen Lv||Wanhui Qian||Longtao Huang||Jizhong Han||Songlin Hu. SAM-Net: Integrating Event-Level and Chain-Level Attentions to Predict What Happens Next. Proceedings of the AAAI Conference on Artificial Intelligence 2019 p.6802-6809.

Shangwen Lv||Wanhui Qian||Longtao Huang||Jizhong Han||Songlin Hu. 2019. SAM-Net: Integrating Event-Level and Chain-Level Attentions to Predict What Happens Next. "Proceedings of the AAAI Conference on Artificial Intelligence". 6802-6809.

Shangwen Lv||Wanhui Qian||Longtao Huang||Jizhong Han||Songlin Hu. (2019) "SAM-Net: Integrating Event-Level and Chain-Level Attentions to Predict What Happens Next", Proceedings of the AAAI Conference on Artificial Intelligence, p.6802-6809

Shangwen Lv||Wanhui Qian||Longtao Huang||Jizhong Han||Songlin Hu, "SAM-Net: Integrating Event-Level and Chain-Level Attentions to Predict What Happens Next", AAAI, p.6802-6809, 2019.

Shangwen Lv||Wanhui Qian||Longtao Huang||Jizhong Han||Songlin Hu. "SAM-Net: Integrating Event-Level and Chain-Level Attentions to Predict What Happens Next". Proceedings of the AAAI Conference on Artificial Intelligence, 2019, p.6802-6809.

Shangwen Lv||Wanhui Qian||Longtao Huang||Jizhong Han||Songlin Hu. "SAM-Net: Integrating Event-Level and Chain-Level Attentions to Predict What Happens Next". Proceedings of the AAAI Conference on Artificial Intelligence, (2019): 6802-6809.

Shangwen Lv||Wanhui Qian||Longtao Huang||Jizhong Han||Songlin Hu. SAM-Net: Integrating Event-Level and Chain-Level Attentions to Predict What Happens Next. AAAI[Internet]. 2019[cited 2023]; 6802-6809.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
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