Papers by Yaushian Wang
English Contrastive Learning Can Learn Universal Cross-lingual Sentence Embeddings (2022.emnlp-main)
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| Challenge: | mSimCSE can learn high-quality universal cross-lingual sentence embeddings without any parallel data. |
| Approach: | They propose a new language-based sentence embedding system that extends SimCSE to multilingual settings. |
| Outcome: | The proposed method improves existing methods on retrieval and multilingual STS tasks. |
Tree Transformer: Integrating Tree Structures into Self-Attention (D19-1)
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| Challenge: | Existing work on hierarchical structure in neural networks has not captured human intuitions about hierarchic structures. |
| Approach: | They propose to add an extra constraint to attention heads of the bidirectional Transformer encoder to encourage attention heads to follow tree structures. |
| Outcome: | The proposed model improves language modeling and learning more explainable attention scores. |
Learning to Encode Text as Human-Readable Summaries using Generative Adversarial Networks (D18-1)
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| Challenge: | a popular approach to learning data representations involves the use of an auto-encoder that compresses data into a latent-space representation without supervision. |
| Approach: | They propose to train an auto-encoder that encodes input text into human-readable sentences . they use comprehensible natural language as a latent representation of the input source text . |
| Outcome: | The proposed auto-encoder can encode input text into human-readable sentences without document-summary pairs. |