Research map / Label-efficient and robust learning / Recognition and long-tail
Improving Word Embeddings with Convolutional Feature Learning and Subword Information
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| remove inflection features (ablation) | Our Model · Google analogy 3CosAdd | accuracy: 38.0 -> 43.7 | Helped |
| remove inflection features (ablation) | Our Model · Microsoft analogy 3CosAdd | accuracy: 46.5 -> 52.5 | Helped |
| use convolutional context layer instead of fully-connected, with all subword features (ablation) | DSSM (l+r+n) · Microsoft analogy 3CosAdd | accuracy: 48.5 -> 52.5 | Helped |
| use convolutional context layer instead of fully-connected, with all subword features (ablation) | DSSM (l+r+n) · Google analogy 3CosAdd | accuracy: 41.7 -> 43.7 | Helped |
| add root/affix and inflection features to letter trigrams (ablation) | Our Model (l) · word similarity and analogy | performance | Helped |
| train on smaller enwik8 instead of enwik9 | Our Model · Microsoft analogy 3CosAdd | accuracy: 52.5 -> 50.0 | Hurt |
| set number of negative samples too small or too large (best 50-100) (ablation) | Our Model · word similarity | Spearman rho | Hurt |
Closest papers by meaning
- A Simple but Tough-to-Beat Baseline for Sentence Embeddings (ICLR 2017 · similarity 0.90)
- Learning Semantic Representations for Novel Words: Leveraging Both Form and Context (AAAI 2019 · similarity 0.90)
- Learning Context-Specific Word/Character Embeddings (AAAI 2017 · similarity 0.90)
- Active Discriminative Text Representation Learning (AAAI 2017 · similarity 0.90)
- Semantic Structure-Based Word Embedding by Incorporating Concept Convergence and Word Divergence (AAAI 2018 · similarity 0.89)
- Contextual Document Embeddings (ICLR 2025 · similarity 0.86)
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