Learning Gender-Neutral Word Embeddings (1809.01496v1)

Abstract: Word embedding models have become a fundamental component in a wide range of NLP applications. However, embeddings trained on human-generated corpora have been demonstrated to inherit strong gender stereotypes that reflect social constructs. To address this concern, in this paper, we propose a novel training procedure for learning gender-neutral word embeddings. Our approach aims to preserve gender information in certain dimensions of word vectors while compelling other dimensions to be free of gender influence. Based on the proposed method, we generate a Gender-Neutral variant of GloVe (GN-GloVe). Quantitative and qualitative experiments demonstrate that GN-GloVe successfully isolates gender information without sacrificing the functionality of the embedding model.

Learning Gender-Neutral Word Embeddings: A Summary

The paper "Learning Gender-Neutral Word Embeddings" by Jieyu Zhao et al. presents a novel methodological advancement in the field of NLP focused on mitigating gender biases inherent in traditional word embedding models. The authors introduce an innovative training procedure designed to isolate and preserve gender information within specific dimensions of word vectors, while ensuring that the remaining dimensions are devoid of gender influence. The result is a new model, referred to as Gender-Neutral GloVe (GN-GloVe), which provides a solution to the problem of gender bias propagation through word embeddings without compromising the utility of the embeddings in downstream applications.

Research Context and Motivation

Traditional word embedding techniques, such as GloVe and word2vec, have been widely adopted due to their capability of capturing semantic relationships within large corpora and representing words in a continuous vector space. However, these models often inherit and inadvertently amplify societal biases present in the training data, such as gender stereotypes, which can lead to biased outcomes in applications ranging from search algorithms to job recommendation systems. The research motivation is to develop a framework that can effectively disentangle gender biases from the embedding space, thus reducing the risk of biased decision-making in automated systems.

Methodology

The authors propose GN-GloVe, a modification of the GloVe model, to tackle gender bias through a dedicated embedding dimension scheme. The approach categorizes word vectors into neutral and gender-specific components. The gendered aspect is confined to a predefined subset of the embedding dimensions, allowing for ease of isolation and removal. This is achieved via regularization terms in the loss function of the GloVe model, which enforce constraints to control the distribution of gender-related information.

The methodological rigor involves the division of vocabulary into subsets of male-definition, female-definition, and gender-neutral words using WordNet definitions. Subsequently, GN-GloVe employs optimization techniques to minimize the gender projection in the neutral components of gender-neutral words while maintaining the intrinsic word relationships captured in the primary GloVe space.

Experimental Results

The experiments conducted demonstrate the effectiveness of GN-GloVe in different scenarios:

1. Bias Reduction: GN-GloVe successfully reduces gender biases as indicated by decreased projections of gender-neutral word vectors onto the gender subspace compared to standard GloVe embeddings. This quantitative reduction correlates with improvements in fairness in downstream applications, such as coreference resolution systems.
2. Maintained Performance: The model's performance on benchmark NLP tasks is shown to remain stable. GN-GloVe achieves comparable results to traditional embeddings on word similarity and analogy tasks, confirming that bias mitigation does not come at the cost of model performance.
3. Downstream Application Fairness: Notably, GN-GloVe demonstrates reduced bias in a coreference resolution task compared to its counterparts, as evidenced by diminished differential performance on gender-stereotyped vs. anti-stereotyped tasks. This supports the utility of GN-GloVe in real-world applications requiring unbiased gender representations.

Implications and Future Work

The implications of this research are significant for both theoretical exploration and practical applications. The embedding technique presents a promising avenue for developing unbiased AI systems. Moreover, it opens up opportunities for integrating similar methodologies for addressing other types of societal stereotypes within NLP models. Future research directions suggested include broadening the attribute scope beyond binary gender paradigms and addressing other linguistic contexts.

In conclusion, the paper provides a substantial contribution to the field of ethical AI by presenting a method for creating gender-neutral embeddings. This work sets a foundation for ongoing efforts to mitigate social biases in machine learning models, fostering a more equitable deployment of NLP technologies.