CHAMP: Efficient Annotation and Consolidation of Cluster Hierarchies (2023.emnlp-demo)
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| Challenge: | Various annotation tasks require a complex hierarchical structure over nodes, where each node is a cluster of items. |
| Approach: | They propose an open source tool that incrementally constructs clusters and hierarchy simultaneously over any type of text. |
| Outcome: | The proposed approach significantly reduces annotation time and guarantees transitivity at the cluster and hierarchy levels. |
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| Challenge: | The first workshop on crowdsourcing for NLP is open to all . |
| Approach: | The first workshop on crowdsourcing annotations for NLP is held at the acl.com . the workshop will focus on methods for aggregating and analysing crowdsourced data for Nl-specific tasks. |
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| Challenge: | Existing methods for document retrieval use contrastive learning on datasets lacking explicit structural information. |
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Climbing the Tower of Treebanks: Improving Low-Resource Dependency Parsing via Hierarchical Source Selection (2021.findings-acl)
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Hie-BART: Document Summarization with Hierarchical BART (2021.naacl-srw)
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| Challenge: | Existing document summarization models do not capture hierarchical structures of documents . proposed model incorporates multi-granularity self-attention (MG-SA) |
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Exploiting Global and Local Hierarchies for Hierarchical Text Classification (2022.emnlp-main)
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| Challenge: | Existing methods encode label hierarchy in a global view, which makes them hard to exploit hierarchical information. |
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