ValNorm Quantifies Semantics to Reveal Consistent Valence Biases Across Languages and Over Centuries

Understand

Word embeddings learn implicit biases from linguistic regularities captured by word co-occurrence statistics.

• By extending methods that quantify human-like biases in word embeddings, we introduceValNorm, a novel intrinsic evaluation task and method to quantify the valence dimension of affect in human-rated word sets from social psychology.
• We apply ValNorm on static word embeddings from seven languages (Chinese, English, German, Polish, Portuguese, Spanish, and Turkish) and from historical English text spanning 200 years.
• ValNorm achieves consistently high accuracy in quantifying the valence of non-discriminatory, non-social group word sets.