Papers with meta-embedding
Frustratingly Easy Meta-Embedding – Computing Meta-Embeddings by Averaging Source Word Embeddings (N18-2)
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| Challenge: | Existing methods for producing word embeddings have shown to produce accurate meta-embeddings from pre-trained source embeddables. |
| Approach: | They propose to use arithmetic mean of two distinct word embedding sets to produce an accurate meta-embedding. |
| Outcome: | The proposed method produces meta-embeddings comparable or better than more complex methods. |
Learning Word Meta-Embeddings by Autoencoding (C18-1)
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| Challenge: | Existing word embeddings have shown superior performance in numerous Natural Language Processing (NLP) tasks, however, their performances vary significantly across different tasks. |
| Approach: | They propose to combine distributed word embeddings to produce more accurate and complete meta-embeddings of words. |
| Outcome: | The proposed meta-embeddings outperform the state-of-the-art in multiple tasks. |
Gender Bias in Meta-Embeddings (2022.findings-emnlp)
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| Challenge: | Existing methods to develop meta-embeddings from source embeddings contain unfair gender-related biases, and how these influence the meta-bedding has not been studied yet. |
| Approach: | They propose to use multiple debiasing methods on a single source embedding to create a gender-based meta-embedding. |
| Outcome: | The proposed method amplifies gender biases compared to input source embeddings. |
Task-oriented Domain-specific Meta-Embedding for Text Classification (2020.emnlp-main)
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| Challenge: | Existing methods neglect domain-specific knowledge and use the same word embedding for each word in all domain-specified datasets. |
| Approach: | They propose a method to incorporate domain-specific and task-oriented information into meta-embeddings by combining pre-trained word embeddings. |
| Outcome: | The proposed method performs well on four text classification datasets and shows that it is compatible with existing methods. |