Papers with clustering tasks
Can Your Model Tell a Negation from an Implicature? Unravelling Challenges With Intent Encoders (2024.acl-long)
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| Challenge: | Conversational systems often rely on embedding models for intent classification and intent clustering tasks. |
| Approach: | They propose a toolkit that gives a more holistic view of intent embedding models by considering three tasks– (1) intent classification, (2) intent clustering, and (3) a novel triplet task. |
| Outcome: | The proposed model improves on the linguistic dimensions while affecting performance on downstream task metrics. |
Going beyond research datasets: Novel intent discovery in the industry setting (2023.findings-eacl)
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| Challenge: | Novel intent discovery automates grouping of similar messages to identify previously unknown intents. |
| Approach: | They propose to use question-only data to improve the intent discovery pipeline . they propose to utilize conversational structure of real-life datasets for clustering . |
| Outcome: | The proposed method gives 33pp performance boost over state-of-the-art model for question only . it also gives 13pp performance increase over the naive baseline model . |
Beyond Coherence: Improving Temporal Consistency and Interpretability in Dynamic Topic Models (2026.findings-eacl)
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| Challenge: | Existing topic models capture bag-of-words statistics but lack semantic priors . interpretability remains shallow, relying on noisy top-word lists that obscure thematic clarity. |
| Approach: | They propose a variational framework to capture more faithful temporal trajectories . they propose to use entropy-regularized optimal transport to align entire topic constellations . |
| Outcome: | The proposed framework captures more faithful temporal trajectories and improves interpretability. |
Pairwise Supervised Contrastive Learning of Sentence Representations (2021.emnlp-main)
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| Challenge: | Recent efforts to improve sentence representation learning have a common weakness . siamese or triplet loss only learns from individual sentence pairs or tripletes . |
| Approach: | They propose a discrimination-based approach to bridge entailment and contradiction understanding with categorical concept encoding. |
| Outcome: | The proposed method outperforms the state-of-the-art method on downstream tasks . it improves 10%–13% on clustering tasks and 5%–6% on STS tasks compared with the previous method . |
Contrastive Learning with Prompt-derived Virtual Semantic Prototypes for Unsupervised Sentence Embedding (2022.findings-emnlp)
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| Challenge: | Recent studies focus on instance-wise contrastive learning, attempting to construct positive pairs with textual data augmentation. |
| Approach: | They propose a novel Contrastive learning method with Prompt-derived Virtual semantic prototypes that constructs virtual semantic prototype to each instance and derives negative prototypes by using the negative form of the prompts. |
| Outcome: | The proposed method performs on semantic textual similarity, transfer, and clustering tasks compared to baselines. |
Map of Encoders – Mapping Sentence Encoders using Quantum Relative Entropy (2026.acl-long)
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| Challenge: | a method to compare and visualise sentence encoders at scale is proposed . we map encoder LLMs using QRE-based feature vectors, which are then projected to 2D . |
| Approach: | They propose a method to compare and visualise sentence encoders at scale by creating a map of encoder . they construct a QRE-based map of sentences covering 1101 publicly available sentence encoded sentences . |
| Outcome: | The proposed method compares sentence encoders at scale by creating a map of encoder models . it shows that the map accurately reflects relationships between encoder and unit base encoder . |
LDIR: Low-Dimensional Dense and Interpretable Text Embeddings with Relative Representations (2025.findings-acl)
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| Challenge: | Existing text embeddings with high dimensions are difficult to trace and interpret. |
| Approach: | They propose low-dimensional and interpretable text embeddings with relative representations that encode semantic meanings in a vector space where similar texts are close together in the representation space. |
| Outcome: | The proposed embeddings outperform existing models on multiple tasks with fewer dimensions and are lowdimensional and dense while maintaining interpretability. |
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. |
PL-MTEB: Polish Massive Text Embedding Benchmark (2026.findings-acl)
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| Challenge: | Text embeddings are used in many NLP tasks, including document clustering, semantic search, question answering, and classification. |
| Approach: | They introduce the Polish Massive Text Embedding Benchmark (PL-MTEB) it is a comprehensive benchmark for text embeddings in the Polish language. |
| Outcome: | The proposed model is based on 30 different NLP tasks in the Polish language. |