Analyzing Word Embeddings with Latent Space Regularization and Semantics Probing

Core Concepts

Regularizing word embeddings through βVAE compresses latent space, improving interpretability and semantics understanding.

Abstract

The content discusses the transformation of high-dimensional word embeddings into a latent space using regularization techniques. It explores the benefits of compressing embeddings for better interpretability and semantic understanding. The article introduces βVAE as a method to regularize the latent space, condensing information and improving semantic saliency. A visual analytics system is designed to monitor the regularization process, explore latent dimensions' semantics, and validate the approach through evaluations.

Introduction

Word embeddings are crucial in natural language processing.
Transforming high-dimensional embeddings into a latent space enhances interpretability.

Embedding Transformation

Regularizing the latent space with βVAE condenses information effectively.
Dimension deprecation phenomenon observed during model convergence.

Semantics Probing

Interactive perturbation method to probe encoding-level of semantics in individual dimensions.
Visual analytics system aids in exploring HD latent space and interpreting semantics.

Model Evaluation

Comparison between AE and βVAE convergence patterns.
Reconstruction loss decreases while regularization loss increases initially.

Latent Dimension Exploration

Identification of deprecated dimensions based on entropy values.
Useful dimensions encode diverse information, while deprecated ones show limited variance.

Semantics Interpretation

Perturbation-based exploration reveals semantic extensions around specific words.
Word cloud visualization showcases dominant semantics in brushed ranges.

Case Study Findings

Model evolution analysis highlights optimization order differences between AE and βVAE.
Latent dimension exploration identifies useful vs deprecated dimensions effectively.

Stats

To preserve the embeddings’ quality, these works often map the embeddings into an even higher-dimensional latent space, making them less interpretable.
We experimented with different β values and found a small β could maintain the quality of reconstructed embeddings.
The number of deprecated dimensions correlates with the value of β.

Quotes

"The embedding transformation process is our focus."
"Our VA system consists of three major components."

Key Insights Distilled From

Compressing and Interpreting Word Embeddings with Latent Space Regularization and Interactive Semantics Probing

by Haoyu Li,Jun... at arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.16815.pdf

Deeper Inquiries

How does dimension deprecation impact model transparency

Dimension deprecation impacts model transparency by reducing the interpretability of the latent space. When dimensions are deprecated, they lose their ability to encode meaningful information, making it challenging for users to understand how semantics are represented in those dimensions. This lack of clarity can hinder the overall transparency of the model as it becomes harder to discern which dimensions are relevant for encoding specific semantic features. As a result, interpreting and explaining the decision-making process of the model becomes more complex and less transparent.

What are potential drawbacks of mapping embeddings into higher-dimensional spaces

Mapping embeddings into higher-dimensional spaces can have several potential drawbacks:

Increased Complexity: Higher-dimensional spaces introduce complexity that may make it difficult to analyze and interpret embeddings effectively.
Reduced Interpretability: The additional dimensions can lead to decreased interpretability as understanding each dimension's contribution becomes more challenging.
Resource Intensive: Storing embeddings in higher-dimensional spaces requires more storage space and computational resources, potentially impacting performance on resource-constrained devices.
Diminished Generalization: Mapping embeddings into excessively high-dimensional spaces may lead to overfitting and reduced generalization capabilities on unseen data.

How can interactive probing enhance semantic understanding beyond word embeddings

Interactive probing enhances semantic understanding beyond word embeddings by allowing users to explore how different semantics are encoded in individual latent dimensions interactively. By perturbing values along specific dimensions related to certain semantics, users can observe how these changes affect reconstructed outputs and measure the angle between user-proposed semantics and regressed reconstructions. This interactive approach provides a deeper insight into how various meanings or concepts are represented within the embedding space, enabling a more nuanced understanding of semantic relationships beyond simple word associations captured by traditional word embeddings alone.