Papers with hierarchical architecture
Document-level Multi-aspect Sentiment Classification by Jointly Modeling Users, Aspects, and Overall Ratings (C18-1)
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| Challenge: | Existing approaches focus on text information, but authors and overall ratings are ignored, both of which are proved to be significant on interpreting the sentiments of different aspects. |
| Approach: | They propose a hierarchical user-aspect rating network model to consider user preference and overall ratings jointly. |
| Outcome: | The proposed model can predict aspects of a product in two real-world datasets. |
Talk to Your Slides: High-Efficiency Slide Editing via Language-Driven Structured Data Manipulation (2026.findings-acl)
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| Challenge: | Talk-to-Your-Slides is a high-efficiency slide editing agent that uses language-driven structured data manipulation instead of the image modality. |
| Approach: | They propose a language-driven slide editing agent that uses language-based structured data manipulation instead of image modality. |
| Outcome: | The proposed system achieves faster processing and better instruction fidelity than GUI-based agents. |
Hierarchical Modeling of Global Context for Document-Level Neural Machine Translation (D19-1)
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| Challenge: | Document-level machine translation (MT) remains challenging due to the difficulty in efficiently using document context. |
| Approach: | They propose a hierarchical model to learn document context for document-level neural machine translation . they use a sentence encoder to capture intra-sentence dependencies and a document encoder . |
| Outcome: | The proposed model significantly improves document-level translation performance over strong baselines. |
Multi-label Categorization of Accounts of Sexism using a Neural Framework (D19-1)
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Pulkit Parikh, Harika Abburi, Pinkesh Badjatiya, Radhika Krishnan, Niyati Chhaya, Manish Gupta, Vasudeva Varma
| Challenge: | Sexism manifests in blatant as well as subtle ways, authors say . existing work on sexism classification has limitations in terms of categories used . authors: categorization of accounts of sexist behavior can aid in countering sextism . |
| Approach: | They propose a neural solution that can combine sentence representations with distributional and linguistic word embeddings. |
| Outcome: | a new method outperforms deep learning and traditional methods by an appreciable margin . the proposed method outpersforms several deep learning as well as traditional baselines by an approval margin compared to baselines . |
A Hierarchical VAE for Calibrating Attributes while Generating Text using Normalizing Flow (2021.acl-long)
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| Challenge: | Existing text-style transfer techniques focus on flipping the style attribute polarity instead of fine-grained regulation of attributes to generate multiple variants of a sentence. |
| Approach: | They propose a hierarchical architecture for finer control over the attribute, preserving content using attribute dis- entanglement. |
| Outcome: | The proposed framework generates natural looking sentences with finer control of intensity of a given attribute. |
Hierarchical CVAE for Fine-Grained Hate Speech Classification (D18-1)
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| Challenge: | Existing work on automated hate speech detection focuses on binary classification or on differentiating among a small set of categories. |
| Approach: | They propose a method to discriminate among 40 hate groups of 13 different hate group categories. |
| Outcome: | The proposed method outperforms discriminative models on a fine-grained hate speech classification task. |
CTFN: Hierarchical Learning for Multimodal Sentiment Analysis Using Coupled-Translation Fusion Network (2021.acl-long)
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| Challenge: | Existing methods for multimodal sentiment analysis require all modalities as input, thus are sensitive to missing modality at predicting time. |
| Approach: | They propose to model bi-direction interplay via couple learning and exploit multiple bi-directional translations to exploit multimodal fusion embeddings. |
| Outcome: | The proposed framework achieves state-of-the-art or often competitive performance on two multimodal benchmarks with extensive ablation studies. |
Improving Document-Level Sentiment Analysis with User and Product Context (2020.coling-main)
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| Challenge: | Existing work that improves document-level sentiment analysis by encoding user and product information has been limited to considering only the text of the current review. |
| Approach: | They propose to incorporate all available historical review text belonging to the author of the review in question and investigate the inclusion of his- torical reviews associated with the current product. |
| Outcome: | The proposed model improves on IMDB, Yelp 2013 and Yelpan 2014 datasets by more than 2 percentage points in the best case. |
Text-to-Multimodal Retrieval with Bimodal Input Fusion in Shared Cross-Modal Transformer (2024.lrec-main)
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| Challenge: | Multimodal video retrieval systems are needed for multimodal content retrieval . multimodal video search systems are sub-optimal for multi-modal content representations . |
| Approach: | They propose a model that learns retrieval cues for the textual query from multiple modalities and a shared embedding space with task-specific contrastive loss functions. |
| Outcome: | The proposed model outperforms state-of-the-art methods on the MSR-VTT and YouCook2 datasets and shows significant improvements from baseline. |
GROKE: Vision-Free Navigation Instruction Evaluation via Graph Reasoning on OpenStreetMap (2026.acl-long)
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| Challenge: | Existing evaluation metrics for navigation instructions are limited by licensing constraints and computational costs. |
| Approach: | They propose a vision-free training-free hierarchical LLM-based framework for evaluating navigation instructions using OpenStreetMap data. |
| Outcome: | The proposed framework reduces navigation error by 68.5% compared to baselines on the Map2Seq dataset. |
Hierarchical Visual Agent: Managing Contexts in Joint Image-Text Space for Advanced Chart Reasoning (2026.findings-acl)
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| Challenge: | Existing MLLMs are strong at understanding single plots, but struggle with multi-step reasoning . Existing approaches to manage context in chart reasoning include text-based chain-of-thought prompting . |
| Approach: | They propose a hierarchical visual agent framework that iteratively constructs a working context in an image–text space. |
| Outcome: | The proposed framework improves on strong multimodal baselines. |