Papers with word and sentence embeddings

4 papers
Deep Bayesian Natural Language Processing (P19-4)

Copied to clipboard

Challenge: Introduction to deep Bayesian learning for natural language addresses the fundamentals of statistical models and neural networks.
Approach: This tutorial addresses the advances in deep Bayesian learning for natural language . it focuses on advanced Bayessian models and deep models . authors present case studies and domain applications to tackle different issues .
Outcome: This tutorial focuses on advanced Bayesian models and deep models for natural language . case studies and domain applications are presented to tackle different issues in deep Bayessian processing, learning and understanding.
A Multi-task Approach to Learning Multilingual Representations (P18-2)

Copied to clipboard

Challenge: Using a multi-task model, we learn word and sentence embeddings in a single task.
Approach: They propose a multi-task modeling approach that trains a skip-gram model and a cross-lingual sentence similarity model to learn word and sentence embeddings together.
Outcome: The proposed model can learn word and sentence embeddings in a multilingual distributed representations of text using a cross-lingual sentence similarity model.
Sentence Mover’s Similarity: Automatic Evaluation for Multi-Sentence Texts (P19-1)

Copied to clipboard

Challenge: Existing automatic metrics for evaluating text are expensive and time-consuming.
Approach: They propose automatic metrics that evaluate text in a continuous space using word and sentence embeddings.
Outcome: The proposed method outperforms ROUGE on machine-generated summaries and human-authored essays on human-generated texts.
UMUTextStats: A linguistic feature extraction tool for Spanish (2022.lrec-1)

Copied to clipboard

Challenge: Feature Engineering is the application of domain knowledge to build efficient machine learning models.
Approach: a team of researchers has developed a linguistic extraction tool for Spanish . the tool uses linguistic features and embeddings to build efficient machine learning models .
Outcome: UMUTextStats is a linguistic extraction tool for Spanish . it has been validated in infodemiology, hate-speech detection, author profiling, authorship verification, humour or irony detection, among others.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations