text similarity
97 papers with code • 0 benchmarks • 3 datasets
Benchmarks
These leaderboards are used to track progress in text similarity
Datasets
DICE: a Dataset of Italian Crime Event news
Most implemented papers
Stacked Cross Attention for Image-Text Matching
Prior work either simply aggregates the similarity of all possible pairs of regions and words without attending differentially to more and less important words or regions, or uses a multi-step attentional process to capture limited number of semantic alignments which is less interpretable.
CAT-Seg: Cost Aggregation for Open-Vocabulary Semantic Segmentation
KU-CVLAB/CAT-Seg
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CVPR 2024
Open-vocabulary semantic segmentation presents the challenge of labeling each pixel within an image based on a wide range of text descriptions.
RETSim: Resilient and Efficient Text Similarity
This paper introduces RETSim (Resilient and Efficient Text Similarity), a lightweight, multilingual deep learning model trained to produce robust metric embeddings for near-duplicate text retrieval, clustering, and dataset deduplication tasks.
Query-based Attention CNN for Text Similarity Map
This network is composed of compare mechanism, two-staged CNN architecture with attention mechanism, and a prediction layer.
Matching Images and Text with Multi-modal Tensor Fusion and Re-ranking
Wangt-CN/MTFN-RR-PyTorch-Code
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We propose a novel framework that achieves remarkable matching performance with acceptable model complexity.
HHH: An Online Medical Chatbot System based on Knowledge Graph and Hierarchical Bi-Directional Attention
14H034160212/HHH-An-Online-Question-Answering-System-for-Medical-Questions
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Proceedings of the Australasian Computer Science Week Multi-conference (ACSW 2020) 2020
This paper proposes a chatbot framework that adopts a hybrid model which consists of a knowledge graph and a text similarity model.
Effective Crowd-Annotation of Participants, Interventions, and Outcomes in the Text of Clinical Trial Reports
Obtaining such a corpus from crowdworkers, however, has been shown to be ineffective since (i) workers usually lack domain-specific expertise to conduct the task with sufficient quality, and (ii) the standard approach of annotating entire abstracts of trial reports as one task-instance (i. e. HIT) leads to an uneven distribution in task effort.
ESimCSE: Enhanced Sample Building Method for Contrastive Learning of Unsupervised Sentence Embedding
caskcsg/sentemb
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COLING 2022
Unsup-SimCSE takes dropout as a minimal data augmentation method, and passes the same input sentence to a pre-trained Transformer encoder (with dropout turned on) twice to obtain the two corresponding embeddings to build a positive pair.
Smoothed Contrastive Learning for Unsupervised Sentence Embedding
caskcsg/sentemb
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COLING 2022
Contrastive learning has been gradually applied to learn high-quality unsupervised sentence embedding.
InfoCSE: Information-aggregated Contrastive Learning of Sentence Embeddings
caskcsg/sentemb
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Contrastive learning has been extensively studied in sentence embedding learning, which assumes that the embeddings of different views of the same sentence are closer.
