Papers with hierarchical models
A Hierarchical Neural Attention-based Text Classifier (D18-1)
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| Challenge: | Existing hierarchical classification models are unable to handle large corpora and the number of categories increases with increasing corpus. |
| Approach: | They propose to use external knowledge to introduce a hierarchical neural attention-based classifier to help with the classification of documents. |
| Outcome: | The proposed model performs better than or comparable to state-of-the-art hierarchical models at significantly lower computational cost while maintaining high interpretability. |
Coupling Local Context and Global Semantic Prototypes via a Hierarchical Architecture for Rhetorical Roles Labeling (2026.eacl-long)
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Anas Belfathi, Nicolas Hernandez, Monceaux Laura, Warren Bonnard, Mary Catherine Lavissière, Christine Jacquin, Richard Dufour
| Challenge: | Hierarchical models capture local dependencies but lack global, corpus-level representations. |
| Approach: | They propose two prototype-based methods that integrate local context with global representations to address this limitation. |
| Outcome: | The proposed methods integrate local context with global representations. |
Numeracy for Language Models: Evaluating and Improving their Ability to Predict Numbers (P18-1)
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| Challenge: | Numeracy is the ability to understand and work with numbers. |
| Approach: | They propose a neural architecture that uses a continuous probability density function to model numerals from an open vocabulary using hierarchical models. |
| Outcome: | The proposed model reduces errors by 18% and 54% on clinical and scientific datasets compared to the second best model for each dataset . |
Modeling Human Sentence Processing with Left-Corner Recurrent Neural Network Grammars (2021.emnlp-main)
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| Challenge: | Existing literature is agnostic about a parsing strategy of hierarchical models . a recent study showed that hierarchically model hierarchic structures capture grammatical dependencies much better than RNNs in targeted syntactic evaluations. |
| Approach: | They evaluated three LMs with head-final left-branching structures and Recurrent Neural Network Grammars with top-down and left-corner parsing strategies as hierarchical models. |
| Outcome: | The proposed model outperforms top-down and left-corner models against human reading times in Japanese. |
“We will Reduce Taxes” - Identifying Election Pledges with Language Models (2021.findings-acl)
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| Challenge: | a political party's manifestos are published before any election, but do they follow through? a new study uses neural models to distinguish between actual pledges and general statements . |
| Approach: | They use election manifestos of Swedish and Indian political parties to learn neural models that distinguish actual pledges from generic positions. |
| Outcome: | The proposed model can predict election year and manifesto's party, while context introduces noise. |
Pretrained Language Models for Sequential Sentence Classification (D19-1)
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| Challenge: | Recent successful models for document-level understanding have used hierarchical encoding and CRFs to capture dependencies between subsequent labels. |
| Approach: | They propose a pretrained language model that captures contextual dependencies without hierarchical encoding nor a CRF. |
| Outcome: | The proposed model captures contextual dependencies without hierarchical encoding nor a CRF on four datasets, including a new dataset of structured scientific abstracts. |
From Characters to Tokens: Dynamic Grouping with Hierarchical BPE (2025.findings-emnlp)
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| Challenge: | Subword tokenization methods suffer from inefficiencies in representing rare words and require large embedding matrices. |
| Approach: | They propose a dynamic character grouping method that leverages existing BPE tokenization without adding additional models. |
| Outcome: | The proposed method matches or exceeds existing entropy- and whitespace-based patching strategies while maintaining a compact vocabulary. |
IRIS: Interpretable Retrieval-Augmented Classification for Long Interspersed Document Sequences (2025.acl-long)
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| Challenge: | Existing models for document classification struggle with long-text processing due to quadratic computational complexity in the self-attention module. |
| Approach: | They propose a framework that utilizes retrieval to efficiently classify long documents . they use a quadratic attention matrix to capture dependencies between tokens in an input sequence . |
| Outcome: | The proposed framework excels in clinical note disease risk prediction tasks . it can process arbitrarily long documents without increasing computational cost and trainable on a single GPU. |