Papers with latent space representation
Large Sequence Representation Learning via Multi-Stage Latent Transformers (2022.coling-1)
Copied to clipboard
| Challenge: | a novel algorithm for named-entity recognition (NER) uses language and spatial features to predict entity tags for structured text . a dataset of 11,926 images depicting food product labels is used to perform NER tasks . |
| Approach: | They propose a multi-stage transformer architecture for named-entity recognition . they propose RADAR, an LSTM classifier operating at character level, to refine NER predictions . |
| Outcome: | The proposed method outperforms two competing models on a food label dataset. |
Class Name Guided Out-of-Scope Intent Classification (2024.findings-emnlp)
Copied to clipboard
Chandan Gautam, Sethupathy Parameswaran, Aditya Kane, Yuan Fang, Savitha Ramasamy, Suresh Sundaram, Sunil Sahu, Xiaoli Li
| Challenge: | SCOOS leverages semantic cues embedded in class labels to improve classification accuracy. |
| Approach: | They propose a method to create a compact feature space around class label semantics . they use a shared latent space between ID features and class names to minimize losses . |
| Outcome: | The proposed method outperforms existing methods for out-of-scope intent detection and ID intent classification. |