On the Downstream Performance of Compressed Word Embeddings

Area of Science:

• Natural Language Processing (NLP)
• Machine Learning
• Data Compression

Background:

• Compressing word embeddings is vital for deploying NLP models in resource-limited environments.
• Current methods for assessing compression quality often fail to predict downstream task performance accurately.
• A reliable metric is needed to guide the selection of effective compressed word embeddings.

Purpose of the Study:

• To introduce and validate the eigenspace overlap score as a novel measure for word embedding compression quality.
• To theoretically link the eigenspace overlap score to the performance of compressed embeddings in regression tasks.
• To demonstrate the practical utility of the eigenspace overlap score in selecting high-performing compressed embeddings.

Main Methods:

• Development of the eigenspace overlap score.
• Derivation of generalization bounds for compressed embeddings using the eigenspace overlap score in linear and logistic regression.
• Lower bounding the eigenspace overlap score for uniform quantization compression.
• Empirical evaluation of the eigenspace overlap score as a selection criterion.

Main Results:

• The eigenspace overlap score is proposed as a new measure for compression quality.
• Generalization bounds connect the eigenspace overlap score to downstream performance in regression.
• The score helps explain the success of uniform quantization compression.
• Using the eigenspace overlap score reduces selection error rates by up to 2x compared to existing measures.

Conclusions:

• The eigenspace overlap score offers a more reliable way to assess compressed word embedding quality.
• This score can guide the selection of embeddings, improving efficiency and performance in NLP applications.
• The theoretical framework provides insights into why certain compression techniques work well.