Papers with context-aware embeddings
Retrieval Enhancements for RAG: Insights from a Deployed Customer Support Chatbot (2026.eacl-industry)
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Daniel González Juclà, Mohit Tuteja, Marcos Esteve Casademunt, Keshav Unnikrishnan, Yasir Usmani, Arvind Roshaan
| Challenge: | a persistent gap remains between Recall@10 and Recall @50 across datasets . |
| Approach: | They evaluate embedding model comparison, Reciprocal Rank Fusion and embedded concatenation techniques to improve retrieval quality. |
| Outcome: | The proposed methods outperform traditional cross-encoders in identifying high-relevance passages. |
Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-shot Dependency Parsing (N19-1)
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| Challenge: | Existing methods for multilingual transfer are limited by their dynamic nature. |
| Approach: | They propose a method that utilizes deep contextual embeddings, pretrained in an unsupervised fashion. |
| Outcome: | The proposed method outperforms the state-of-the-art on 6 languages, yielding an improvement of 6.8 LAS points on average. |
A Context-Aware Contrastive Learning Framework for Hateful Meme Detection and Segmentation (2025.findings-naacl)
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| Challenge: | Empirical experiments show HateSieve surpasses existing LMMs in performance with fewer trainable parameters . |
| Approach: | They propose a framework to enhance detection and segmentation of hateful elements in memes by creating a triplet dataset and an Image-Text Alignment module. |
| Outcome: | HateSieve features a new framework that creates semantically correlated memes and generates contextual embeddings for accurate meme segmentation. |
On the Robustness of Offensive Language Classifiers (2022.acl-long)
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| Challenge: | Existing studies on offensive language classifiers have focused on primitive attacks such as misspellings and extraneous spaces. |
| Approach: | They analyze the robustness of offensive language classifiers against crafty adversarial attacks that leverage greedy- and attention-based word selection and context-aware embeddings for word replacement. |
| Outcome: | The proposed classifiers are robust against more crafty attacks that leverage greedy- and attention-based word selection and context-aware embeddings for word replacement. |
HyperKGR: Knowledge Graph Reasoning in Hyperbolic Space with Graph Neural Network Encoding Symbolic Path (2025.emnlp-main)
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| Challenge: | Existing methods for linking knowledge graphs are incomplete and rely on Euclidean embeddings . a hyperbolic GNN framework embeds recursive learning trees in hyperbolical space . |
| Approach: | They propose a hyperbolic GNN framework that embeds recursive learning trees in hyperbolical space and generates query-specific embeddings. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on multiple benchmark datasets. |