Papers STS
“STS” 태그가 달린 논문 334편 · 필터 해제
DALR: Dual-level Alignment Learning for Multimodal Sentence Representation Learning
Previous multimodal sentence representation learning methods have achieved impressive performance. However, most approaches focus on aligning images and text at a coarse level, facing two critical challenges:cross-modal …
cross-modal alignmentRepresentation LearningSemantic SimilaritySemantic Textual Similarity+2Intrinsic vs. Extrinsic Evaluation of Czech Sentence Embeddings: Semantic Relevance Doesn't Help with MT Evaluation
In this paper, we compare Czech-specific and multilingual sentence embedding models through intrinsic and extrinsic evaluation paradigms. For intrinsic evaluation, we employ Costra, a complex sentence transformation data…
Machine TranslationSemantic SimilaritySemantic Textual SimilaritySentence+5Intelligent Travel Activity Monitoring: Generalized Distributed Acoustic Sensing Approaches
Obtaining data on active travel activities such as walking, jogging, and cycling is important for refining sustainable transportation systems (STS). Effectively monitoring these activities not only requires sensing solut…
Federated LearningMeta-LearningPrivacy PreservingSTSGATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training
Semantic textual similarity (STS) is a critical task in natural language processing (NLP), enabling applications in retrieval, clustering, and understanding semantic relationships between texts. However, research in this…
MTEB BenchmarkNatural Language InferenceRepresentation LearningSemantic Similarity+3Contrastive Prompting Enhances Sentence Embeddings in LLMs through Inference-Time Steering
Extracting sentence embeddings from large language models (LLMs) is a practical direction, as it requires neither additional data nor fine-tuning. Previous studies usually focus on prompt engineering to guide LLMs to enc…
Prompt EngineeringSemantic Textual SimilaritySentenceSentence Embedding+3Parameter-Efficient Transformer Embeddings
Embedding layers in transformer-based NLP models typically account for the largest share of model parameters, scaling with vocabulary size but not yielding performance gains proportional to scale. We propose an alternati…
Natural Language InferenceSentenceSentence-EmbeddingSTS+1Global Stress Generation and Spatiotemporal Super-Resolution Physics-Informed Operator under Dynamic Loading for Two-Phase Random Materials
Material stress analysis is a critical aspect of material design and performance optimization. Under dynamic loading, the global stress evolution in materials exhibits complex spatiotemporal characteristics, especially i…
STSSuper-ResolutionMapping Controversies Using Artificial Intelligence: An Analysis of the Hamas-Israel Conflict on YouTube
This article analyzes the Hamas-Israel controversy through 253,925 Spanish-language YouTube comments posted between October 2023 and January 2024, following the October 7 attack that escalated the conflict. Adopting an i…
STSOn the (im)possibility of sustainable artificial intelligence. Why it does not make sense to move faster when heading the wrong way
Artificial intelligence (AI) is currently considered a sustainability "game-changer" within and outside of academia. In order to discuss sustainable AI this article draws from insights by critical data and algorithm stud…
STSCASE -- Condition-Aware Sentence Embeddings for Conditional Semantic Textual Similarity Measurement
The meaning conveyed by a sentence often depends on the context in which it appears. Despite the progress of sentence embedding methods, it remains unclear how to best modify a sentence embedding conditioned on its conte…
Dimensionality ReductionLanguage ModelingLanguage ModellingLarge Language Model+7Domain Adaptation for Japanese Sentence Embeddings with Contrastive Learning based on Synthetic Sentence Generation
Several backbone models pre-trained on general domain datasets can encode a sentence into a widely useful embedding. Such sentence embeddings can be further enhanced by domain adaptation that adapts a backbone model to a…
Contrastive LearningDomain AdaptationSemantic Textual SimilaritySentence+2TextInPlace: Indoor Visual Place Recognition in Repetitive Structures with Scene Text Spotting and Verification
Visual Place Recognition (VPR) is a crucial capability for long-term autonomous robots, enabling them to identify previously visited locations using visual information. However, existing methods remain limited in indoor …
Robot NavigationSTStext similarityText Spotting+1AutoTestForge: A Multidimensional Automated Testing Framework for Natural Language Processing Models
In recent years, the application of behavioral testing in Natural Language Processing (NLP) model evaluation has experienced a remarkable and substantial growth. However, the existing methods continue to be restricted by…
FairnessSemantic Textual SimilaritySentiment AnalysisSTSFinMTEB: Finance Massive Text Embedding Benchmark
Embedding models play a crucial role in representing and retrieving information across various NLP applications. Recent advances in large language models (LLMs) have further enhanced the performance of embedding models. …
ArticlesSemantic Textual SimilaritySTSPropNet: a White-Box and Human-Like Network for Sentence Representation
Transformer-based embedding methods have dominated the field of sentence representation in recent years. Although they have achieved remarkable performance on NLP missions, such as semantic textual similarity (STS) tasks…
Semantic Textual SimilaritySentenceSTSNon-Interchangeability between Heart Rate Variability and Pulse Rate Variability During Supine-to-Stand Tests
Heart rate variability (HRV) is widely recognized as a valuable biomarker for assessing autonomic cardiac regulation. Pulse rate variability (PRV) is a common surrogate of HRV given the wide usability of PPG in commercia…
Heart Rate VariabilitySTS2-Tier SimCSE: Elevating BERT for Robust Sentence Embeddings
Effective sentence embeddings that capture semantic nuances and generalize well across diverse contexts are crucial for natural language processing tasks. We address this challenge by applying SimCSE (Simple Contrastive …
Contrastive LearningSemantic Textual SimilaritySentenceSentence Embeddings+3Token Prepending: A Training-Free Approach for Eliciting Better Sentence Embeddings from LLMs
Extracting sentence embeddings from large language models (LLMs) is a promising direction, as LLMs have demonstrated stronger semantic understanding capabilities. Previous studies typically focus on prompt engineering to…
Prompt EngineeringSemantic Textual SimilaritySentenceSentence Embedding+3Detecting Redundant Health Survey Questions Using Language-agnostic BERT Sentence Embedding (LaBSE)
The goal of this work was to compute the semantic similarity among publicly available health survey questions in order to facilitate the standardization of survey-based Person-Generated Health Data (PGHD). We compiled va…
Computational EfficiencyQuestion SimilaritySemantic SimilaritySemantic Textual Similarity+6Fast, Precise Thompson Sampling for Bayesian Optimization
Thompson sampling (TS) has optimal regret and excellent empirical performance in multi-armed bandit problems. Yet, in Bayesian optimization, TS underperforms popular acquisition functions (e.g., EI, UCB). TS samples arms…
Bayesian OptimizationSTSThompson Sampling