Papers › Analytical Methods for Interpretable Ultradense Word Embeddings

Analytical Methods for Interpretable Ultradense Word Embeddings

18 Apr 2019IJCNLP 2019 11arXiv:1904.08654archive 2025-07-28

Philipp Dufter, Hinrich Schütze

Word embeddings are useful for a wide variety of tasks, but they lack interpretability. By rotating word spaces, interpretable dimensions can be identified while preserving the information contained in the embeddings without any loss. In this work, we investigate three methods for making word spaces interpretable by rotation: Densifier (Rothe et al., 2016), linear SVMs and DensRay, a new method we propose. In contrast to Densifier, DensRay can be computed in closed form, is hyperparameter-free and thus more robust than Densifier. We evaluate the three methods on lexicon induction and set-based word analogy. In addition we provide qualitative insights as to how interpretable word spaces can be used for removing gender bias from embeddings.

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