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CoVe

Contextual Word Vectors

Natural Language ProcessingIntroduced 200013 papers

Description

CoVe, or Contextualized Word Vectors, uses a deep LSTM encoder from an attentional sequence-to-sequence model trained for machine translation to contextualize word vectors. $\text{CoVe}$ word embeddings are therefore a function of the entire input sequence. These word embeddings can then be used in downstream tasks by concatenating them with $\text{GloVe}$ embeddings:

$v = \left[\text{GloVe}\left(x\right), \text{CoVe}\left(x\right)\right]$

and then feeding these in as features for the task-specific models.

Papers Using This Method

CoVE: Compressed Vocabulary Expansion Makes Better LLM-based Recommender Systems2025-06-24 COVE: COntext and VEracity prediction for out-of-context images2025-02-03 On the Role of Surrogates in Conformal Inference of Individual Causal Effects2024-12-16 COVE: Unleashing the Diffusion Feature Correspondence for Consistent Video Editing2024-06-13 A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models2024-01-02 Chain-of-Verification Reduces Hallucination in Large Language Models2023-09-20 Learning Category Trees for ID-Based Recommendation: Exploring the Power of Differentiable Vector Quantization2023-08-31 FastFusionNet: New State-of-the-Art for DAWNBench SQuAD2019-02-28 Language Modeling Teaches You More than Translation Does: Lessons Learned Through Auxiliary Syntactic Task Analysis2018-11-01 Language Modeling Teaches You More Syntax than Translation Does: Lessons Learned Through Auxiliary Task Analysis2018-09-26 Improving Matching Models with Hierarchical Contextualized Representations for Multi-turn Response Selection2018-08-22 Jiangnan at SemEval-2018 Task 11: Deep Neural Network with Attention Method for Machine Comprehension Task2018-06-01 Learned in Translation: Contextualized Word Vectors2017-08-01