A Causal Inference Method for Reducing Gender Biasin Word Embedding Relations
Table 4: Average Pearson correlation coefficient of semantic textual similarity tasks each year
['[EMPTY]', '[BOLD] GloVe', '[BOLD] Post-Processing  [BOLD] Hard-GloVe', '[BOLD] Post-Processing  [BOLD] GP-GloVe', '[BOLD] Post-Processing  [BOLD] HSR-GloVe', '[BOLD] WV-Learning  [BOLD] GN-GloVe']
The semantic textual similarity (STS) tasks examine the sentence-level effectiveness of word embeddings by measuring the degree of semantic equivalence between two texts [Agirre2012]. The STS tasks we employ include 20 tasks from 2012 SemEval Semantic Related task (SICK) and SemEval STS tasks from 2012 to 2015 [Marco2014, Agirre2012, Agirre2013, Agirre2014, Agirre2015]. For each task, we average the embedding of words in the sentence as the sentence embedding, and record the Pearson correlation coefficient of the estimated rankings of sentence similarity against the human rankings. For the SICK task, HSR-GloVe performs the best among all post-processing methods. On average, HSR-GloVe achieves an improvement of 14.19% over the 20 STS tasks with respect to the prior best post-processing methods, and it attains 14.71% improvement with respect to the original embedding.