Papers with fine-grained classification
Fusion Makes Perfection: An Efficient Multi-Grained Matching Approach for Zero-Shot Relation Extraction (2024.naacl-short)
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| Challenge: | Existing methods to extract unseen relations require laborious manual annotation . a new approach uses fine-grained matching to reduce manual annotation cost . |
| Approach: | They propose an efficient multi-grained matching approach that uses virtual entity matching to reduce manual annotation cost. |
| Outcome: | The proposed approach outperforms the state-of-the-art methods and achieves inference efficiency and accuracy in zero-shot relation extraction tasks. |
Quality Estimation for Partially Subjective Classification Tasks via Crowdsourcing (2020.lrec-1)
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| Challenge: | a common approach to quality estimation is to ask multiple reviewers to evaluate the same artifacts. |
| Approach: | They propose a probabilistic model for subjective classification tasks that incorporates the qualities of artifacts as well as the abilities and biases of creators and reviewers as latent variables to be jointly inferred. |
| Outcome: | The proposed model estimates the quality of speech more effectively than a vote aggregation, measured by correlation with a fine-grained classification by experts. |
Towards Fine-grained Classification of Climate Change related Social Media Text (2022.acl-srw)
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| Challenge: | a new study examines the fine-grained classification and classification of climate change-related social media text. |
| Approach: | They propose to use two datasets to analyze climate change-related social media text and propose a fine-grained classification based on the proposed dataset. |
| Outcome: | The proposed datasets are compared with existing datasets and benchmarked using the best-performing model. |
A Fully Hyperbolic Neural Model for Hierarchical Multi-Class Classification (2020.findings-emnlp)
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| Challenge: | Existing models for fine-grained entity typing have a hierarchical structure . prior work has integrated only explicit hierarchic information by formulating a hierarchy-aware loss or by representing instances and labels in a joint Euclidean embedding space. |
| Approach: | They propose a fully hyperbolic model for multi-class multi-label classification that performs all operations in hyperbolical space. |
| Outcome: | The proposed model performs all operations in hyperbolic space on two challenging datasets and shows it is comparable to state-of-the-art methods on fine-grained classification with remarkable reduction of parameter size. |
Coarse2Fine: Fine-grained Text Classification on Coarsely-grained Annotated Data (2021.emnlp-main)
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| Challenge: | Existing text classification methods focus on a fixed label set, but many real-world applications require extending to new fine-grained classes as the number of samples per label increases. |
| Approach: | They propose a problem called coarse-to-fine grained classification that leverages label surface names as the only human guidance. |
| Outcome: | The proposed method outperforms existing methods on two real-world datasets. |
MM-MATH: Advancing Multimodal Math Evaluation with Process Evaluation and Fine-grained Classification (2024.findings-emnlp)
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| Challenge: | Existing benchmarks for multimodal reasoning in large multimodal models are underperforming on multimodal tasks. |
| Approach: | They propose a benchmark for multimodal reasoning in large multimodal models, MM-MATH . MM's process evaluation employs LMM-as-a-judge to automatically analyze solution steps . diagram misinterpretation is the most common error, they find . |
| Outcome: | The proposed model achieves only 31% accuracy, compared to 82% for humans. |
Contrastive Bootstrapping for Label Refinement (2023.acl-short)
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| Challenge: | Existing methods for fine-grained classification categorize texts into coarse-gritty classes, but they are suboptimal in real-world scenarios. |
| Approach: | They propose a lightweight contrastive clustering-based bootstrapping method to iteratively refine the labels of passages. |
| Outcome: | The proposed method outperforms the state-of-the-art methods by a large margin on NYT and 20News datasets. |
Distinguishability Calibration to In-Context Learning (2023.findings-eacl)
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| Challenge: | Recent studies have shown that pre-trained language models generate similar output embeddings which makes it difficult to discriminate for the prompt-based classifier. |
| Approach: | They propose a calibration method which rotates the embedding feature into a new metric space and adapts the ratio of each dimension to a uniform distribution. |
| Outcome: | The proposed method improves the distinguishability of learning embeddings on three datasets under various settings. |
PEEB: Part-based Image Classifiers with an Explainable and Editable Language Bottleneck (2024.findings-naacl)
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| Challenge: | CLIP-based classifiers rely on the prompt containing a class name that is known to the text encoder and perform poorly on new classes or the classes whose names rarely appear on the Internet. |
| Approach: | They propose to use a set of text descriptors to express a class name into a textual descriptable and match the embeddings of the detected parts to their textual ones to compute a logit score. |
| Outcome: | The proposed classifier outperforms CLIP-based classifiers on zero-shot and supervised learning settings by 88.80% and 92.20% accuracy on CUB-200 and Stanford Dogs-120. |
Attention for Implicit Discourse Relation Recognition (L18-1)
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| Challenge: | Existing approaches to implicit discourse relation recognition reach F1 scores of 9.95% to 37.67% . a neural network exploits the strong correlation between pairs of words that implicitly signal a discourse relation. |
| Approach: | They propose a neural network which exploits strong correlation between pairs of words . they use an encoder-decoder model with attention to detect a latent discourse relation . |
| Outcome: | The proposed model outperforms state-of-the-art models on fine-grained classification and fine-granular classification while computing parameters without pooling and fully connected layers. |
Spotlighter: Revisiting Prompt Tuning from a Representative Mining View (2025.findings-emnlp)
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| Challenge: | Spotlighter is a lightweight token-selection framework that enhances accuracy and efficiency in prompt tuning. |
| Approach: | They propose a token-selection framework that enhances accuracy and efficiency in prompt tuning by preserving only the top-scoring tokens for downstream prediction. |
| Outcome: | The proposed framework outperforms CLIP by up to 11.19% in harmonic mean accuracy and achieves 0.8K additional FPS, with only 21 extra parameters. |
Multi-Dialect Vietnamese: Task, Dataset, Baseline Models and Challenges (2024.emnlp-main)
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| Challenge: | Vietnamese is a low-resource language, but each province has its own distinct pronunciation variations. |
| Approach: | They propose a dataset that captures the rich diversity of 63 provincial dialects spoken in Vietnam. |
| Outcome: | The proposed dataset captures the rich diversity of 63 provincial dialects spoken across Vietnam. |
Few-Shot Open-Set Classification via Reasoning-Aware Decomposition (2025.emnlp-main)
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| Challenge: | Large language models excel at few-shot learning, but their ability to reject out-of-distribution examples remains under-explored. |
| Approach: | They introduce a novel amortised Generative Flow Network framework that uses latent trajectories to approximate the Bayesian posterior. |
| Outcome: | The proposed framework can generalise with as few as 4 examples per class, enabling Llama 3.2 3B to achieve up to 80% of the performance of Llma 3.3 70B in complex datasets. |
Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection (2020.lrec-1)
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| Challenge: | Prior fake news datasets lack multimodal text and image data, metadata, comment data, and fine-grained classification at the scale and breadth of their datasets. |
| Approach: | They propose to use a multimodal dataset to build a machine learning classification model that uses text and image data to classify fake news. |
| Outcome: | The proposed model is based on a multimodal dataset consisting of over 1 million samples from multiple categories of fake news. |
Mapping the Circumplex of Affect: Geometric Analysis of Emotion Representations via Hyperspherical Contrastive Learning (2026.acl-long)
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| Challenge: | Existing methods to induce circular emotion representations in language models are limited . elucidates trade-offs involved in applying circumplex models to deep learning architectures . |
| Approach: | They propose a method to induce circular emotion representations within language models via contrastive learning on a hypersphere. |
| Outcome: | The proposed method underperforms in high-dimensional settings and fine-grained classification. |
Explaining Matters: Leveraging Definitions and Semantic Expansion for Sexism Detection (2025.acl-long)
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| Challenge: | Existing tools for sexism detection fail to capture subtle distinctions within sexist content, limiting their practical applicability. |
| Approach: | They propose two techniques to address class imbalance and nuanced nature of sexist language . definition-based data augmentation leverages category-specific definitions to generate semantically-aligned examples . |
| Outcome: | The proposed techniques improve accuracy across all tasks and improve reliability. |
Leveraging Label Semantics and Entity Description Generation for LLM-based Fine-grained Entity Typing (2026.findings-acl)
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| Challenge: | Fine-grained entity typing (FET) aims to assign semantically rich and contextually appropriate types to entity mentions. |
| Approach: | They propose a descriptor-based retrieval-augmented framework that reduces effective label space . they propose to use natural language descriptores as an intermediate semantic representation . |
| Outcome: | The proposed framework outperforms existing methods under noisy supervision. |