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Sentence Retrieval

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Showing 26–50 of 82 papers

TitleStatusHype
Is Cross-lingual Evaluation Only About Cross-lingual?—0
Language Features Matter: Effective Language Representations for Vision-Language Tasks—0
Learning a Recurrent Visual Representation for Image Caption Generation—0
Learning Joint Representations of Videos and Sentences with Web Image Search—0
Learning to Scale Multilingual Representations for Vision-Language Tasks—0
MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers—0
Answer Sentence Retrieval by Matching Dependency Paths acquired from Question/Answer Sentence Pairs—0
Approximate Sentence Retrieval for Scalable and Efficient Example-Based Machine Translation—0
Mind's Eye: A Recurrent Visual Representation for Image Caption Generation—0
MULE: Multimodal Universal Language Embedding—0
Cross-modal Semantic Enhanced Interaction for Image-Sentence Retrieval—0
Deep Fragment Embeddings for Bidirectional Image Sentence Mapping—0
OneAligner: Zero-shot Cross-lingual Transfer with One Rich-Resource Language Pair for Low-Resource Sentence Retrieval—0
OneAligner: Zero-shot Cross-lingual Transfer with One Rich-Resource Language Pair for Low-Resource Sentence Retrieval—0
Parallel Sentence Retrieval From Comparable Corpora for Biomedical Text Simplification—0
Preserving Cross-Linguality of Pre-trained Models via Continual Learning—0
Privacy, Interpretability, and Fairness in the Multilingual Space—0
Automatic Concept Discovery from Parallel Text and Visual Corpora—0
Robust Fragment-Based Framework for Cross-lingual Sentence Retrieval—0
SEAGLE: A Platform for Comparative Evaluation of Semantic Encoders for Information Retrieval—0
sEEG-based Encoding for Sentence Retrieval: A Contrastive Learning Approach to Brain-Language Alignment—0
Semantic Alignment with Calibrated Similarity for Multilingual Sentence Embedding—0
SetCSE: Set Operations using Contrastive Learning of Sentence Embeddings—0
SPLADE-v3: New baselines for SPLADE—0
Soft Language Clustering for Multilingual Model Pre-training—0
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