Axis Tour: Word Tour Determines the Order of Axes in ICA-transformed Embeddings
This addresses interpretability challenges in NLP for researchers and practitioners, but it is incremental as it builds on existing ICA and Word Tour methods.
The paper tackles the problem of arbitrary axis order in ICA-transformed word embeddings by proposing Axis Tour, a method that optimizes axis order to maximize semantic continuity, resulting in better or comparable low-dimensional embeddings compared to PCA and ICA in downstream tasks.
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.