1 BIAS IN WORD VECTORS
Table 6: Performance on ECT, EQT and WEAT by the different debiasing methods; and performance on standard similarity and analogy tests.
['analogy head', 'original', 'HD', 'flipping 0.5', 'flipping 0.75', 'flipping 1.0', 'subtraction word pairs', 'subtraction names', 'projection word pairs', 'projection names']
Flipping does nothing to affect the names, so it is not surprising that it does not improve this score; further indicating that it is challenging to directly fix bias in raw text before creating embeddings. Moreover, HD has the lowest score (of 0.917) whereas projection obtains scores of 0.996 (with vB) and 0.943 (with vB,names). (WSim, 353 word pairs) and SimLex-999 (Simlex, 999 word pairs) We also use the Google Analogy Dataset using the function 3COSADD