Papers › Dynamic Meta-Embeddings for Improved Sentence Representations
Dynamic Meta-Embeddings for Improved Sentence Representations
Douwe Kiela, Changhan Wang, Kyunghyun Cho
While one of the first steps in many NLP systems is selecting what pre-trained word embeddings to use, we argue that such a step is better left for neural networks to figure out by themselves. To that end, we introduce dynamic meta-embeddings, a simple yet effective method for the supervised learning of embedding ensembles, which leads to state-of-the-art performance within the same model class on a variety of tasks. We subsequently show how the technique can be used to shed new light on the usage of word embeddings in NLP systems.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Natural Language Inference | SNLI | 512D Dynamic Meta-Embeddings | % Test Accuracy | 86.7 | #52 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | 512D Dynamic Meta-Embeddings | % Train Accuracy | 91.6 | #52 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | 512D Dynamic Meta-Embeddings | Parameters | 9m | #52 of 98 | Archive leaderboard | report |
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