Axis Tour: Word Tour Determines the Order of Axes in ICA-transformed Embeddings

Abstract: Word embedding is one of the most important components in natural language processing, but interpreting high-dimensional embeddings remains a challenging problem. To address this problem, Independent Component Analysis (ICA) is identified as an effective solution. ICA-transformed word embeddings reveal interpretable semantic axes; however, the order of these axes are arbitrary. In this study, we focus on this property and propose a novel method, Axis Tour, which optimizes the order of the axes. Inspired by Word Tour, a one-dimensional word embedding method, we aim to improve the clarity of the word embedding space by maximizing the semantic continuity of the axes. Furthermore, we show through experiments on downstream tasks that Axis Tour yields better or comparable low-dimensional embeddings compared to both PCA and ICA.

• The paper introduces Axis Tour, which orders ICA axes by cosine similarity to maximize semantic continuity in word embeddings.
• It applies an innovative methodology that aligns semantic axes based on measured continuity and outperforms PCA and standard ICA in downstream NLP tasks.
• Experimental results demonstrate improved word similarity, analogy, and categorization performance, highlighting practical benefits for NLP applications.

Introduction to Word Embeddings and Interpretability Challenges

Word embeddings are fundamental to many tasks in the field of NLP. However, interpreting the high-dimensional data that make up these embeddings often presents a considerable challenge. Independent Component Analysis (ICA) has been established as a meaningful way to transform word embeddings into components that can be interpreted as semantic axes. Nonetheless, one limitation with ICA is that the order of the resulting axes is arbitrary, which can hinder clear semantic interpretation. The introduction of the Axis Tour approach aims to resolve this by organizing axes based on their semantic continuity.

Understanding Axis Tour

Axis Tour is inspired by Word Tour, a technique for creating one-dimensional word embeddings with meaningful continuity. In the Axis Tour approach, the axes obtained from the ICA transformation of word embeddings are reordered to maximize semantic continuity. This way, the most significant words of the axes are systematically arranged for clearer interpretability. For instance, words with similar meanings or topics are set to be adjacent in the ordered axes, creating a resilient semantic flow of ideas.

Methodology and Technical Insights

Axis Tour defines an "axis embedding" for each axis, representing its underlying semantic meaning. These embeddings are then positioned in order based on their cosine similarities, with the objective to maximize similarities of adjacent axis embeddings. A further dimensionality reduction then projects the data onto a lower-dimensional space while preserving semantic relatedness. The effectiveness of Axis Tour in ordering and reducing dimensions was compared with other methods like PCA and standard ICA using several experimental setups. Notably, the Axis Tour technique showed improved performance in downstream tasks, signifying its ability to create a more coherent embedding space.

Experimental Results and Observations

Experimentation revealed that Axis Tour successfully orders axes in a way that adjacent axes often share closer semantic meanings. For example, an axis concerning Eastern European countries would seamlessly transition to axes containing words related to Germany, then France, and so on, evincing geographical and cultural continuity. Histograms of cosine similarities between adjacent axis embeddings indicated that Axis Tour provides more consistently high similarities compared to baselines. Additionally, the new technique outperformed other methods in analogy, word similarity, and categorization tasks, suggesting that Axis Tour’s method of merging semantically similar axes leads to more efficient lower-dimensional embeddings.

Concluding Remarks

The introduction of Axis Tour demonstrates a significant advancement in improving the interpretability of high-dimensional word embeddings. By optimally ordering axes and enhancing the clarity of the embedding space, Axis Tour supports more coherent lower-dimensional representations, which have shown superior performance in various NLP tasks. Despite its promising results, future work could explore optimizing dimension reduction vectors further, considering non-linear transformations, and adapting the division points of axes for semantic coherence.