| Challenge: | a high annotation cost for dependency parsers is a challenge . batch active learning (AL) is based on batch mode, which is more efficient for annotators to label in bulk. |
| Approach: | They propose to reduce the number of labeled examples needed to train a strong dependency parser using batch active learning. |
| Outcome: | The proposed approach improves on an English newswire corpus by enforcing diversity in the sampled batches. |
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Active2 Learning: Actively reducing redundancies in Active Learning methods for Sequence Tagging and Machine Translation (2021.naacl-main)
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| Challenge: | Existing approaches to deep learning for NLP require large amounts of labeled data. |
| Approach: | They propose an approach that iteratively selects a small number of examples for expert annotation based on their estimated utility in training the model. |
| Outcome: | The proposed approach reduces the data requirements of state-of-the-art AL strategies by 3-25% on multiple NLP tasks while achieving the same performance with virtually no additional computation overhead. |
Hallucination Diversity-Aware Active Learning for Text Summarization (2024.naacl-long)
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| Challenge: | Existing methods for alleviating hallucinations require costly human annotations . Existing approaches focus on a specific type of hallucinism, which limits their effectiveness . |
| Approach: | They propose a method to detect hallucinations from errors in semantic frame, discourse and content verifiability in LLM summarization using HAllucination Diversity-Aware Sampling. |
| Outcome: | The proposed framework reduces the need for costly human annotations to correct hallucinations in LLM outputs. |
D-CALM: A Dynamic Clustering-based Active Learning Approach for Mitigating Bias (2023.findings-acl)
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| Challenge: | Infusing clustering with active learning with AL can overcome the bias issue of both AL and traditional annotation methods while exploiting AL’s annotation efficiency. |
| Approach: | They propose an algorithm that dynamically adjusts clustering and annotation efforts in response to an estimated classifier error-rate. |
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To Label or Not to Label: Hybrid Active Learning for Neural Machine Translation (2025.coling-main)
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| Challenge: | Active learning (AL) techniques reduce labeling costs for training neural machine translation models by selecting smaller representative subsets from unlabeled data for annotation. |
| Approach: | They propose an AL strategy that combines uncertainty and diversity for sentence selection. |
| Outcome: | The proposed method prioritizes diverse instances having high model uncertainty for annotation in early iterations. |
Structurally Diverse Sampling for Sample-Efficient Training and Comprehensive Evaluation (2022.findings-emnlp)
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| Challenge: | Existing approaches to generalize compositionally are inadequate, but there is no evidence for this. |
| Approach: | They propose a model-agnostic algorithm for subsampling instances with diverse structures from a labeled instance pool with structured outputs. |
| Outcome: | The proposed algorithm leads to comparable or better generalization than prior algorithms in 9 out of 10 dataset-split type pairs. |
Automatically Selecting the Best Dependency Annotation Design with Dynamic Oracles (N18-2)
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| Challenge: | Multiple annotation conventions have been proposed for representing dependency structures. |
| Approach: | They propose to consider a set of syntactic references encoding alternative syntak representations to train a parser with a dynamic oracle. |
| Outcome: | The proposed approach can predict the best syntactic representation among all possible references. |
Parser Training with Heterogeneous Treebanks (P18-2)
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| Challenge: | In the 2017 CoNLL Shared Task on Universal Dependency Parsing, 25 languages have more than one treebank . many teams did not take advantage of the multiple treebanks, however, and trained one model per treebank instead of one model for each language. |
| Approach: | They propose a method to make the most of heterogeneous treebanks when training a monolingual parser. |
| Outcome: | The proposed method improves on training with multiple treebanks for a single language. |
Can Data Diversity Enhance Learning Generalization? (2022.coling-1)
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| Challenge: | a diversity advanced actor-critical reinforcement learning framework is used to improve NLP generalization and accuracy. |
| Approach: | They introduce Diversity Advanced Actor-Critic reinforcement learning framework to improve NLP generalization and accuracy. |
| Outcome: | The proposed framework outperforms domain adaptation and generalization baselines without using any target domain knowledge. |
A Closer Look into the Robustness of Neural Dependency Parsers Using Better Adversarial Examples (2021.findings-acl)
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| Challenge: | Neural network-based models have been successful in a wide range of NLP tasks, but their performance is undermined by adversarial examples that would pose no confusion for humans. |
| Approach: | They propose a method to generate high-quality adversarial examples with a higher number of candidate generators and stricter filters and then verify their quality using automatic and human evaluations. |
| Outcome: | The proposed method improves the robustness of English parsing models by relying on adversarial training and model ensembling. |
Principled Content Selection to Generate Diverse and Personalized Multi-Document Summaries (2025.acl-long)
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| Challenge: | Large language models exhibit the _”lost in the middle” phenomenon when they are unevenly attending to different parts of the provided context. |
| Approach: | They propose principled content selection as a way to increase source coverage . they use determinantal point processes to prioritize diverse content . |
| Outcome: | The proposed method improves source coverage on the DiverseSumm benchmark. |