most citedEnhancing Interpretability using Human Similarity Judgements to Prune Word Embeddings

1 citations · 1 across the 2 of their papers we have counts for

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2 papers

cs.CV2024

Explaining Human Comparisons using Alignment-Importance Heatmaps

Nhut Truong, Dario Pesenti, Uri Hasson

We present a computational explainability approach for human comparison tasks, using Alignment Importance Score (AIS) heatmaps derived from deep-vision models. The AIS reflects a f…

cs.CL2023★ 1 cited

Enhancing Interpretability using Human Similarity Judgements to Prune Word Embeddings

Natalia Flechas Manrique, Wanqian Bao, Aurelie Herbelot +1

Interpretability methods in NLP aim to provide insights into the semantics underlying specific system architectures. Focusing on word embeddings, we present a supervised-learning m…