| 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. |
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| Challenge: | Existing approaches to domain-specific taxonomy induction from text are relying on distributional semantics for hyponym-hypernym relationships, but many of them learn prototypical hypernymes, not taking into account the relation between both terms in classification. |
| Approach: | They propose to use Poincaré embeddings to improve existing approaches to domain-specific taxonomy induction from text as a signal for relocating wrong hyponym terms and attaching disconnected terms in a taxonomies. |
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Find Parent then Label Children: A Two-stage Taxonomy Completion Method with Pre-trained Language Model (2023.eacl-main)
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| Challenge: | Existing taxonomies focus on adding concepts to the leaf nodes of the existing tree, which does not fully utilize the taxonomy’s knowledge and is unable to update the original taxomy structure. |
| Approach: | They propose a two-stage method called ATTEMPT for taxonomy completion that inserts new concepts into the correct position by finding a parent node and labeling child nodes. |
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Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Auto-Encoders (N19-1)
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| Challenge: | Using the deep inside-outside recursive autoencoder, we can extract both shallow parses and full syntactic trees from any domain or language automatically. |
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Categorial Grammar Induction with Stochastic Category Selection (2024.lrec-main)
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| Challenge: | categorial grammar inducers have been used to learn from raw data, but they use shortcuts to ensure branching behavior. |
| Approach: | They propose a grammar inducer that learns from raw data and does not rely on bias terms . they show a recall-homogeneity of 0.48 on a corpus of English child-directed speech . |
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TEMP: Taxonomy Expansion with Dynamic Margin Loss through Taxonomy-Paths (2021.emnlp-main)
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| Challenge: | Existing taxonomies are unable to maintain coverage due to the rising of new concepts . TEMP uses pre-trained contextual encoders to predict the position of new ideas . |
| Approach: | They propose a self-supervised taxonomy expansion method that ranks taxonomies by ranking them . they use pre-trained contextual encoders to train the model with dynamic margin loss . |
| Outcome: | The proposed method outperforms state-of-the-art taxonomy expansion methods by 14.3% and 15.8% on public benchmarks. |
Learning to Retrieve Iteratively for In-Context Learning (2024.emnlp-main)
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Yunmo Chen, Tongfei Chen, Harsh Jhamtani, Patrick Xia, Richard Shin, Jason Eisner, Benjamin Van Durme
| Challenge: | In-context learning is a powerful tool for learning large language models. |
| Approach: | They propose an iterative retrieval framework that empowers retrievers to make iterable decisions through policy optimization. |
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Learning Sentence Representations over Tree Structures for Target-Dependent Classification (N18-1)
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| Challenge: | Existing work on tree structures uses syntactic parsers or Treebank annotations to perform target-dependent classifications. |
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| Outcome: | The proposed model gives superior performance on two benchmark tasks compared to previous work on parsed trees . |
End-to-end Deep Reinforcement Learning Based Coreference Resolution (P19-1)
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| Challenge: | Recent neural network models for coreference resolution are usually trained with heuristic loss functions that are computed over a sequence of local decisions. |
| Approach: | They propose an end-to-end reinforcement learning based coreference resolution model to directly optimize coreference evaluation metrics. |
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A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions (2026.acl-long)
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Zhiyin Yu, Yuchen Mou, Juncheng Yan, Junyu Luo, Chunchun Chen, Xing Wei, Yunhui Liu, Hongru Sun, Yuxing Zhang, Jun Xu, Yatao Bian, Ming Zhang, Wei Ye, Tieke He, Jie Yang, Guanjie Zheng, Zhonghai Wu, Bo Zhang, Lei Bai, Xiao Luo
| Challenge: | Existing research on reinforcement learning for LLMs under data scarcity has not been unified. |
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Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning (P19-1)
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| Challenge: | Abstract meaning representations (AMRs) are labeled directed acyclic graphs that represent a non intersentential abstraction of natural language with broad-coverage semantic representations. |
| Approach: | They build upon a transition-based AMR parser that uses Stack-LSTMs and augment training with policy learning. |
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