Papers with negative sampling methods
Improving Cross-Domain Chinese Word Segmentation with Word Embeddings (N19-1)
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| Challenge: | Existing approaches to Chinese word segmentation (CWS) are character-based and word-based . character-driven approaches use conditional random field models to label sequences, with complex hand-crafted discrete features. |
| Approach: | They propose a semi-supervised word-based approach to improve cross-domain Chinese word segmentation given a baseline segmenter. |
| Outcome: | The proposed model outperforms state-of-the-art approaches on five datasets covering domains in novels, medicine, and patent. |
Noise Contrastive Estimation and Negative Sampling for Conditional Models: Consistency and Statistical Efficiency (D18-1)
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| Challenge: | Conditional models are frequently encountered in practice, but there has not been a rigorous theoretical analysis of NCE in this setting. |
| Approach: | They propose to use a ranking-based and ranking-only method for conditional models to estimate parameter estimates. |
| Outcome: | The proposed method avoids calculation of partition function or derivatives at each training step . it is closely related to negative sampling methods, now widely used in NLP . |
Learning Dense Representations of Phrases at Scale (2021.acl-long)
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| Challenge: | Existing phrase retrieval models rely on sparse representations and still underperform retriever-reader approaches. |
| Approach: | They propose a method to learn phrase representations from reading comprehension tasks using negative sampling methods. |
| Outcome: | The proposed model improves over previous models by 15%-25% absolute accuracy and matches the performance of state-of-the-art retrieval models. |