| Challenge: | Existing annotation schemes aim at acquiring completely annotated structures, but partial annotations can be costly and hinder learning. |
| Approach: | They propose a method to find out that learning from partial structures can sometimes outperform learning from complete ones. |
| Outcome: | The proposed method outperforms existing methods in three different structured learning tasks. |
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| Challenge: | Structured prediction is a fundamental problem in NLP, wherein the label space consists of complex structured outputs with groups of interdependent variables. |
| Approach: | They propose a partial annotation approach that selects only the most informative sub-structures for annotation and a method that incorporates the current model's automatic predictions as pseudo-labels for un-annotated sub-structurals. |
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Evaluating D-MERIT of Partial-annotation on Information Retrieval (2024.emnlp-main)
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Royi Rassin, Yaron Fairstein, Oren Kalinsky, Guy Kushilevitz, Nachshon Cohen, Alexander Libov, Yoav Goldberg
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Learning Joint Semantic Parsers from Disjoint Data (N18-1)
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| Challenge: | Various formal meaning representations have been developed corresponding to different semantic theories. |
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Constrained Multi-Task Learning for Bridging Resolution (2022.acl-long)
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| Challenge: | bridging resolution is the task of recognizing and resolving bridling anaphors in a text. |
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What can we learn from Semantic Tagging? (D18-1)
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| Challenge: | a recent study shows that multi-task learning improves performance of NLP tasks by exploiting similarities between tasks. |
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A Few More Examples May Be Worth Billions of Parameters (2022.findings-emnlp)
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| Challenge: | Recent work on few-shot learning for natural language tasks explores the dynamics of scaling up either the number of model parameters or labeled examples while controlling for the other variable by setting it to a constant. |
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Learning Constraints for Structured Prediction Using Rectifier Networks (2020.acl-main)
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| Challenge: | Various natural language processing tasks require domain expertise to design good constraints. |
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Reassessing Active Learning Adoption in Contemporary NLP: A Community Survey (2026.eacl-long)
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Weaker Than You Think: A Critical Look at Weakly Supervised Learning (2023.acl-long)
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| Challenge: | Weakly supervised learning is a popular approach for training machine learning models in low-resource settings. |
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Towards Dynamic Computation Graphs via Sparse Latent Structure (D18-1)
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| Challenge: | Existing approaches to learn latent structure are limited by factorization assumptions or end-to-end differentiability. |
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