Papers with autoencoder-generated representations
Similarity-Based Reconstruction Loss for Meaning Representation (D18-1)
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| Challenge: | a new paper proposes and evaluates a set of loss functions that can be used to train models for representation learning . cross-entropy loss penalizes models when they fail to generate the exact word from ground truth data . |
| Approach: | They propose and evaluate loss functions that can be used to train any neural model for representation learning. |
| Outcome: | The proposed loss functions amplify semantic diversity while preserving original meaning . they show performance improvement on paraphrase detection and language inference tasks . |