Sentence Embeddings
219 papers with code • 0 benchmarks • 11 datasets
Benchmarks
These leaderboards are used to track progress in Sentence Embeddings
Libraries
Use these libraries to find Sentence Embeddings models and implementationsDatasets
WikiMatrix
ScanRefer Dataset
Discovery
SemEval-2014 Task-10
Subtasks
Latest papers
Threat Behavior Textual Search by Attention Graph Isomorphism
As such, analysts often resort to text search techniques to identify existing malware reports based on the symptoms they observe, exploiting the fact that malware samples share a lot of similarity, especially those from the same origin.
Simple Techniques for Enhancing Sentence Embeddings in Generative Language Models
Sentence Embedding stands as a fundamental task within the realm of Natural Language Processing, finding extensive application in search engines, expert systems, and question-and-answer platforms.
Decoupling Static and Hierarchical Motion Perception for Referring Video Segmentation
In fact, static cues can sometimes interfere with temporal perception by overshadowing motion cues.
Debiasing Sentence Embedders through Contrastive Word Pairs
themody/debiasing-sentence-embedders-through-contrastive-word-pairs
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It is problematic that most debiasing approaches are directly transferred from word embeddings, therefore these approaches fail to take into account the nonlinear nature of sentence embedders and the embeddings they produce.
KDMCSE: Knowledge Distillation Multimodal Sentence Embeddings with Adaptive Angular margin Contrastive Learning
Previous work on multimodal sentence embedding has proposed multimodal contrastive learning and achieved promising results.
Making Sentence Embeddings Robust to User-Generated Content
NLP models have been known to perform poorly on user-generated content (UGC), mainly because it presents a lot of lexical variations and deviates from the standard texts on which most of these models were trained.
More Discriminative Sentence Embeddings via Semantic Graph Smoothing
chakib401/smoothing_sentence_embeddings
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This paper explores an empirical approach to learn more discriminantive sentence representations in an unsupervised fashion.
UMBCLU at SemEval-2024 Task 1A and 1C: Semantic Textual Relatedness with and without machine translation
The aim of SemEval-2024 Task 1, "Semantic Textual Relatedness for African and Asian Languages" is to develop models for identifying semantic textual relatedness (STR) between two sentences using multiple languages (14 African and Asian languages) and settings (supervised, unsupervised, and cross-lingual).
UNSEE: Unsupervised Non-contrastive Sentence Embeddings
asparius/unsee
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The introduction of the target network allows us to leverage non-contrastive objectives, maintaining training stability while achieving performance improvements comparable to contrastive objectives.
Cross-lingual neural fuzzy matching for exploiting target-language monolingual corpora in computer-aided translation
The paper presents an automatic evaluation of these techniques on four language pairs that shows that our approach can successfully exploit monolingual texts in a TM-based CAT environment, increasing the amount of useful translation proposals, and that our neural model for estimating the post-editing effort enables the combination of translation proposals obtained from monolingual corpora and from TMs in the usual way.
