Papers with ancestor F1
End-to-End Reinforcement Learning for Automatic Taxonomy Induction (P18-1)
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| Challenge: | Existing methods for automating taxonomy induction often divide the problem into two subtasks . a novel end-to-end reinforcement learning approach is proposed to improve the accuracy of such methods. |
| Approach: | They propose an end-to-end reinforcement learning approach to automatic taxonomy induction from a set of terms. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on two public datasets of different domains. |
Constructing Taxonomies from Pretrained Language Models (2021.naacl-main)
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| Challenge: | A variety of NLP tasks use taxonomic information, including question answering and information retrieval. |
| Approach: | They propose a method for constructing taxonomic trees using pretrained language models by incorporating web-retrieved glosses into the model. |
| Outcome: | The proposed model achieves 66.7 ancestor F1, a 20.0% relative increase over the previous best published model on English WordNet. |