Abstract
This paper proposes to study a fine-grained semantic novelty detection task, which can be illustrated with the following example. It is normal that a person walks a dog in the park, but if someone says “A man is walking a chicken in the park,” it is novel. Given a set of natural language descriptions of normal scenes, we want to identify descriptions of novel scenes. We are not aware of any existing work that solves the problem. Although existing novelty or anomaly detection algorithms are applicable, since they are usually topic-based, they perform poorly on our fine-grained semantic novelty detection task. This paper proposes an effective model (called GAT-MA) to solve the problem and also contributes a new dataset. Experimental evaluation shows that GAT-MA outperforms 11 baselines by large margins.
Original language | English (US) |
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Title of host publication | EMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing, Proceedings |
Publisher | Association for Computational Linguistics (ACL) |
Pages | 866-882 |
Number of pages | 17 |
ISBN (Electronic) | 9781955917094 |
State | Published - 2021 |
Externally published | Yes |
Event | 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021 - Virtual, Punta Cana, Dominican Republic Duration: Nov 7 2021 → Nov 11 2021 |
Publication series
Name | EMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing, Proceedings |
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Conference
Conference | 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021 |
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Country/Territory | Dominican Republic |
City | Virtual, Punta Cana |
Period | 11/7/21 → 11/11/21 |
Bibliographical note
Publisher Copyright:© 2021 Association for Computational Linguistics