Equalizing Gender Biases in Neural Machine Translation with Word Embeddings Techniques (1901.03116v2)

Abstract: Neural machine translation has significantly pushed forward the quality of the field. However, there are remaining big issues with the output translations and one of them is fairness. Neural models are trained on large text corpora which contain biases and stereotypes. As a consequence, models inherit these social biases. Recent methods have shown results in reducing gender bias in other natural language processing tools such as word embeddings. We take advantage of the fact that word embeddings are used in neural machine translation to propose a method to equalize gender biases in neural machine translation using these representations. Specifically, we propose, experiment and analyze the integration of two debiasing techniques over GloVe embeddings in the Transformer translation architecture. We evaluate our proposed system on the WMT English-Spanish benchmark task, showing gains up to one BLEU point. As for the gender bias evaluation, we generate a test set of occupations and we show that our proposed system learns to equalize existing biases from the baseline system.

Equalizing Gender Bias in Neural Machine Translation with Word Embeddings Techniques

The paper by Joel Escudé Font and Marta R. Costa-jussà investigates a critical issue in neural machine translation (NMT): the propagation of gender bias through machine learning models trained on biased large text corpora. By exploiting word embeddings that integrate debiasing techniques, the authors provide a methodology to address and mitigate gender biases inherent in NMT systems.

Background and Methodology

The research begins by analyzing the foundational components of NMT systems, utilizing the Transformer architecture known for its effectiveness in sequence-to-sequence learning tasks. This architecture relies heavily on self-attention mechanisms and embeddings that represent words in continuous vector spaces. While these word embeddings capture semantic relationships, they are inherently susceptible to biases reflecting societal stereotypes, such as gender biases outlined by Bolukbasi et al. (2016).

To counter these biases, the authors employed two debiasing methods on word embeddings: Hard-Debiasing as a post-processing technique and GN-GloVe, which aims to produce gender-neutral representations during training. These pre-trained word embeddings were integrated into the Transformer model at different stages—in the encoder, decoder, or both.

Experimental Framework and Results

The empirical evaluation utilized the WMT English-Spanish task, showcasing the system's ability to overcome baseline translation outcomes with an improvement of up to one BLEU point. A custom test set, focusing on occupations and their gender implications, was generated for deeper gender bias analysis. This nuanced evaluation revealed that the debiased word embeddings, specifically GN-GloVe, enhanced translation equality without compromising quality, as evidenced by augmented BLEU scores and more accurate gender pronoun translations in contextually challenging sentences.

The paper demonstrates clear implications for the practical deployment of machine translation systems in multilingual environments. By highlighting the necessity of debiasing in embedding techniques, the authors set a precedent for inclusion and fair representation in AI-driven language technologies.

Implications and Future Directions

The implications of this paper extend beyond technical improvement; they touch upon ethical considerations central to AI deployment in language technologies. The proposed debiasing techniques pave the way for more equitable and responsibly designed machine translation systems. Future research may explore additional biases, such as racial or cultural stereotypes, within MT systems and expand the scope across diverse language pairs and domains.

These developments could lead to more sophisticated models that actively engage with bias reduction as part of their core functionality. By addressing potential amplifications of bias at the aggregate corpus level, researchers can further refine methodologies and contribute to societal equity through computational innovations.

In sum, this paper serves as a significant contribution to the ongoing discourse on fairness in machine learning applications, providing essential insights into embedding techniques aimed at reducing gender bias in neural machine translation systems.