Papers with Siamese neural network architecture
Multifaceted Domain-Specific Document Embeddings (2021.naacl-demos)
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| Challenge: | Current document embeddings require large training corpora but fail to learn high-quality representations when confronted with a small number of domain-specific documents and rare terms. |
| Approach: | They propose a faceted domain encoder that transforms each document into a single embedding vector . they use a Siamese neural network architecture to leverage knowledge graphs to enhance the embeddables . |
| Outcome: | The proposed model achieves the same embedding quality as state-of-the-art models while requiring only a tiny fraction of training data. |
Are You Convinced? Choosing the More Convincing Evidence with a Siamese Network (P19-1)
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Martin Gleize, Eyal Shnarch, Leshem Choshen, Lena Dankin, Guy Moshkowich, Ranit Aharonov, Noam Slonim
| Challenge: | Recent advances in argument detection have made it easier to identify the more convincing arguments. |
| Approach: | They propose a new data set of pairs of evidence labeled for convincingness that is more challenging than existing alternatives. |
| Outcome: | The proposed method outperforms baselines on convincingness data and its own. |