Certain embedding types outperform others in different scenarios, e.g., subword-based embeddings can model rare words well and domain-specific embeddings can better represent in-domain terms. Therefore, recent works consider attention-based meta-embeddings to combine different embedding types. We demonstrate that these methods have two shortcomings: First, the attention weights are calculated without knowledge of word properties. Second, the different embedding types can form clusters in the common embedding space, preventing the computation of a meaningful average of different embeddings and thus, reducing performance. We propose to solve these problems by using feature-based meta-embeddings learned with adversarial training. Our experiments and analysis on sentence classification and sequence tagging tasks show that our approach is effective. We set the new state of the art on various datasets across languages and domains.
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