Dynamic Meta-Embeddings for Improved Sentence Representations
Douwe Kiela, Changhan Wang, Kyunghyun Cho · 2018
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 stateof-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.