Evaluating the Underlying Gender Bias in Contextualized Word Embeddings (1904.08783v1)

Abstract: Gender bias is highly impacting natural language processing applications. Word embeddings have clearly been proven both to keep and amplify gender biases that are present in current data sources. Recently, contextualized word embeddings have enhanced previous word embedding techniques by computing word vector representations dependent on the sentence they appear in. In this paper, we study the impact of this conceptual change in the word embedding computation in relation with gender bias. Our analysis includes different measures previously applied in the literature to standard word embeddings. Our findings suggest that contextualized word embeddings are less biased than standard ones even when the latter are debiased.

Assessing Gender Bias in Contextualized Word Embeddings

This paper presents a comprehensive evaluation of gender bias within contextualized word embeddings, addressing a critical issue in NLP. The authors focus on assessing and comparing contextualized embeddings with traditional word embeddings—both debiased and non-debiased—to understand their impact on gender bias.

Gender Bias in NLP

Gender bias in NLP systems manifests as skewed performance and prejudiced outputs that reflect societal stereotypes. This is particularly significant as NLP applications permeate numerous technological platforms, including machine translation and sentiment analysis. Traditional word embeddings have been shown to harbor and amplify such biases due to their origin in human-generated corpora.

Contextualized Word Embeddings

Recent advancements in word embedding techniques have brought contextualized word embeddings to the forefront, offering representations influenced by the surrounding text. Unlike static embeddings, these representations adjust based on sentence-level context, potentially altering bias dynamics.

Methodological Approach

The paper adopts several established methods for bias detection, adapting them to the nuanced nature of contextualized embeddings. This involves:

1. Principal Component Analysis (PCA): To capture the gender direction, the paper explores PCA on vector differences of gender-defining word pairs. Results indicate a reduced bias vector prominence compared to static embeddings.
2. Direct Bias Measurement: Contextualized embeddings show a lower direct bias value (0.03) relative to the static embeddings (0.08), suggesting decreased proximity to gender vectors.
3. Clustering and Classification: Through clustering and classification experiments on gender-biased words, contextualized embeddings demonstrate less pronounced male/female clustering than their debiased counterparts. Classification accuracy is moderately high, indicating residual implicit bias.
4. K-Nearest Neighbors: An analysis of professional stereotyped words using k-nearest neighbors underscores continuing bias, with stereotyped male or female words clustering with similar biased words.

Implications and Conclusions

The findings affirm that contextualized word embeddings inherently mitigate certain gender biases compared to static embeddings. This mitigation is particularly notable in gender space reduction and direct bias measurements. Nevertheless, contextualized embeddings maintain substantial predictable gender bias, as indicated by clustering and classification tests. Thus, while they reduce gender stereotype parallels, implicit biases persist, warranting further debiasing strategies.

The implications are crucial for developing equitable machine learning models, as lesser bias in contextual representations could lead to fairer NLP applications. Future research should aim at refining debiasing techniques within contextual representations and extending this evaluation model across various languages and domains, furthering the effort towards unbiased AI.

This discourse provides a foundational benchmark for forthcoming studies and methodologies aimed at achieving unbiased language technologies, ensuring that societal biases are minimized in NLP outputs.