Papers with clustering task
Is Anisotropy Truly Harmful? A Case Study on Text Clustering (2023.acl-short)
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| Challenge: | Contextualized pre-trained representations are widely used as input to various tasks such as information retrieval, anomaly detection and document clustering. |
| Approach: | They propose to examine the impact of different transformations on isotropy and performance to assess the true impact of anisotropi. |
| Outcome: | The proposed model is based on a clustering task and shows that it has limited impact on expressiveness and closeness. |
ClusterFormer: Neural Clustering Attention for Efficient and Effective Transformer (2022.acl-long)
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| Challenge: | Existing sparse attention methods use fixed patterns to select words without considering similarities between words. |
| Approach: | They propose a neural clustering method which integrates into the Self-Attention Mechanism in Transformer and integrates it into the target task. |
| Outcome: | The proposed method outperforms two typical sparse attention methods on translation, text classification, and text matching tasks while having a comparable or even better time and memory efficiency. |
Picking Apart Story Salads (D18-1)
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| Challenge: | Story salads are mixtures of multiple documents that can be generated at scale . they exhibit challenging inference problems, and require global context and coherence . |
| Approach: | They propose to generate salads that exhibit challenging inference problems by exploiting the Wikipedia hierarchy . they propose a task where the objective is to group sentences from the same narratives . |
| Outcome: | The proposed task is based on a novel, challenging clustering task using Wikipedia . it is difficult to identify relevant information and assemble it into coherent narratives . |
LLM-OREF: An Open Relation Extraction Framework Based on Large Language Models (2025.emnlp-main)
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| Challenge: | Existing studies focus on building models that can only handle predefined relations . however, their reliance on human annotation limits their practicality . |
| Approach: | They propose an open relation extraction framework that can generalize to new relations not encountered during training. |
| Outcome: | The proposed framework can generalize to new relations not encountered during training. |
SPILL: Domain-Adaptive Intent Clustering based on Selection and Pooling with Large Language Models (2025.findings-acl)
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| Challenge: | Existing methods for intent clustering rely on labeled examples or unsupervised fine-tuning to optimize results for each new dataset. |
| Approach: | They propose a method that uses an embedder to derive an embedding for each utterance and then pool them with the seed to improve the embeddable results. |
| Outcome: | The proposed method outperforms embedding methods and is comparable to state-of-the-art methods. |
IntenDD: A Unified Contrastive Learning Approach for Intent Detection and Discovery (2023.findings-emnlp)
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| Challenge: | Intent-related tasks are typically modeled as separate tasks, but a unified approach is proposed . INTENDD uses an entirely unsupervised contrastive learning strategy for representation learning . |
| Approach: | They propose a unified approach to identifying intents from dialogue utterances . they propose an unsupervised contrastive learning strategy for representation learning . |
| Outcome: | The proposed approach outperforms baselines on three intent-related tasks on multiple datasets. |