Papers with textual models
Personalized Review Recommendation based on Implicit dimension mining (2024.naacl-short)
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| Challenge: | Existing research on review recommendation ignores personalized and implicit requirements. |
| Approach: | They propose a Large language model driven Personalized Review Recommendation model based on Implicit dimension mining. |
| Outcome: | The proposed model outperforms state-of-the-art textual models on the Amazon-MRHP dataset. |
Router-Suggest: Dynamic Routing for Multimodal Auto-Completion in Visually-Grounded Dialogs (2026.eacl-industry)
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| Challenge: | a task that grounds predictions in multimodal context is essential for chatbots, chatbot systems and healthcare consultations. |
| Approach: | They propose a task that grounds predictions in multimodal context to better capture user intent. |
| Outcome: | The proposed task can be used to predict upcoming characters in live chats using partially typed text and visual cues. |
Text Adversarial Purification as Defense against Adversarial Attacks (2023.acl-long)
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| Challenge: | Existing methods for defending against adversarial examples are difficult due to the discrete nature of texts. |
| Approach: | They propose a novel adversarial purification method that aims to remove adversarials and make correct predictions based on the recovered clean samples. |
| Outcome: | The proposed method can defend against word-substitution adversarial attacks using language models. |
LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding (2022.acl-long)
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| Challenge: | Existing structured document understanding models only deal with document data of specific language(s) this is extremely limited for other languages, especially in the case of lacking pre-training structured document data. |
| Approach: | They propose a language-independent Layout Transformer (LiLT) for structured document understanding . they propose to pre-train structured documents in a single language and fine-tune them on other languages . |
| Outcome: | The proposed model achieves competitive or even superior performance on diverse downstream benchmarks on eight languages. |