Papers with cross-attention modules
Neural News Recommendation with Collaborative News Encoding and Structural User Encoding (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing news recommendation models encode news title and content separately without leveraging the structural correlation of user browsing histories to reflect user interests explicitly. |
| Approach: | They propose a news recommendation framework consisting of collaborative news encoding and structural user encode to enhance news and user representation learning. |
| Outcome: | The proposed framework improves the performance of news recommendation on the MIND dataset. |
LVPruning: An Effective yet Simple Language-Guided Vision Token Pruning Approach for Multi-modal Large Language Models (2025.findings-naacl)
Copied to clipboard
| Challenge: | Multi-modal Large Language Models (MLLMs) incur significant computational overhead due to the large number of vision tokens processed, limiting their practicality in resource-constrained environments. |
| Approach: | They propose a language-guided vision token pruning method that can be integrated into existing MLLMs with minimal architectural changes. |
| Outcome: | The proposed method reduces vision tokens by 90% and preserves model performance. |
Cross-Align: Modeling Deep Cross-lingual Interactions for Word Alignment (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing word alignment models capture few interactions between input sentence pairs, which severely degrades the word alignment quality. |
| Approach: | They propose to model deep interactions between input and target sentences using a two-stage training framework to train the model. |
| Outcome: | The proposed model achieves the state-of-the-art (SOTA) performance on four out of five language pairs. |
Empowering Backbone Models for Visual Text Generation with Input Granularity Control and Glyph-Aware Training (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing text-to-image models struggle to generate images with legible visual texts . current models lack support for Chinese texts, misspelling, and lack of diversity . |
| Approach: | They propose to empower backbone models to generate visual texts in Chinese and English . they propose to augment conventional training objective with glyph-aware training losses . |
| Outcome: | The proposed methods can generate visual texts in English and Chinese while maintaining image generation quality. |