Learning Word Embeddings
21 papers with code • 0 benchmarks • 0 datasets
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Then, based on this insight, we propose a novel framework WordRank that efficiently estimates word representations via robust ranking, in which the attention mechanism and robustness to noise are readily achieved via the DCG-like ranking losses.
In this paper, we propose a novel deep neural network architecture, Speech2Vec, for learning fixed-length vector representations of audio segments excised from a speech corpus, where the vectors contain semantic information pertaining to the underlying spoken words, and are close to other vectors in the embedding space if their corresponding underlying spoken words are semantically similar.
Knowledge graphs are structured representations of facts in a graph, where nodes represent entities and edges represent relationships between them.
Our analysis suggests that INSTRUCTOR is robust to changes in instructions, and that instruction finetuning mitigates the challenge of training a single model on diverse datasets.
The Mixing method: low-rank coordinate descent for semidefinite programming with diagonal constraints
In this paper, we propose a low-rank coordinate descent approach to structured semidefinite programming with diagonal constraints.
Learning word embeddings on large unlabeled corpus has been shown to be successful in improving many natural language tasks.
In this study, we improve grammatical error detection by learning word embeddings that consider grammaticality and error patterns.
We present a pointwise mutual information (PMI)-based approach to formalize paraphrasability and propose a variant of PMI, called MIPA, for the paraphrase acquisition.