Evaluating and Debiasing a Dutch Coreference Resolution System for Gender-Neutral Pronouns

Core Concepts

Gender-neutral pronouns are increasingly being introduced in Dutch, but current coreference resolution systems perform worse on these pronouns compared to gendered counterparts. Debiasing techniques like Counterfactual Data Augmentation can substantially reduce this performance gap.

Abstract

The paper examines the performance of a Dutch coreference resolution system on gender-neutral pronouns like hen and die, and explores two debiasing techniques to improve the model's handling of these pronouns.
Key highlights:

The model currently performs worse on gender-neutral pronouns compared to gendered pronouns like hij and zij.
The authors propose a new evaluation metric, the pronoun score, which directly measures the percentage of correctly resolved pronouns.
Counterfactual Data Augmentation (CDA), which involves replacing gendered pronouns with gender-neutral ones in the training data, substantially reduces the performance gap between gendered and gender-neutral pronouns.
CDA remains effective even when using a limited set of debiasing documents, making it a viable debiasing strategy with minimal resources.
The debiased models do not generalize well to previously unseen neopronouns, indicating the need for further research in this area.

Stats

79.1% of all third-person pronouns in the corpus are masculine.
None of the gender-neutral pronouns or neopronouns appear as third-person singular pronouns in the corpus.

Quotes

"Gender-neutral pronouns are increasingly introduced and popularised across Western languages, as suitable alternatives to traditional gendered pronouns for non-binary individuals."
"Recent works have started to investigate non-binary gender biases in NLP systems."
"Our results reveal diminished performance on gender-neutral pronouns compared to gendered counterparts."
"CDA substantially reduces the performance gap between gendered and gender-neutral pronouns."

Key Insights Distilled From

Transforming Dutch: Debiasing Dutch Coreference Resolution Systems for Non-binary Pronouns

by Goya van Bov... at arxiv.org 05-02-2024

https://arxiv.org/pdf/2405.00134.pdf

Deeper Inquiries

How can the debiasing techniques be extended to improve performance on previously unseen neopronouns?

In order to improve performance on previously unseen neopronouns, the debiasing techniques can be extended by incorporating a more dynamic and adaptive approach. One way to achieve this is by implementing continual learning strategies that allow the model to continuously update and adapt to new pronouns as they emerge. By regularly exposing the model to a diverse range of pronouns, including neopronouns, it can learn to generalize patterns and associations more effectively.
Additionally, leveraging transfer learning techniques can be beneficial in enhancing the model's ability to process unseen neopronouns. By pre-training the model on a larger and more diverse dataset that includes a wide range of pronouns, including neopronouns, the model can develop a more robust understanding of different pronoun forms and improve its performance on unseen pronouns.
Furthermore, incorporating data augmentation methods specifically tailored to introduce and familiarize the model with new neopronouns can also be effective. By generating synthetic data that includes instances of neopronouns and integrating them into the training data, the model can learn to recognize and process these pronouns more accurately.
Overall, by adopting a proactive and adaptive approach that focuses on continual learning, transfer learning, and data augmentation, the debiasing techniques can be extended to enhance the model's performance on previously unseen neopronouns.

What other factors, beyond pronouns, contribute to the marginalization of non-binary individuals in NLP systems, and how can those be addressed?

In addition to pronouns, several other factors contribute to the marginalization of non-binary individuals in NLP systems. One significant factor is the lack of representation and diversity in training data, which often leads to biased and inaccurate predictions for non-binary individuals. To address this, it is essential to ensure that training datasets are more inclusive and representative of diverse gender identities, including non-binary individuals. This can be achieved by actively collecting and annotating data that includes a wide range of gender expressions and identities.
Another factor is the presence of gender stereotypes and biases in language models, which can perpetuate harmful and discriminatory attitudes towards non-binary individuals. To mitigate this, it is crucial to implement bias detection and mitigation techniques during model training and evaluation. By identifying and correcting biases in the training data and model predictions, NLP systems can provide more equitable and inclusive outcomes for non-binary individuals.
Moreover, the design and evaluation of NLP systems should consider the intersectionality of gender identity with other social factors, such as race, ethnicity, and socio-economic status. By taking a holistic approach to understanding and addressing bias in NLP systems, we can create more fair and inclusive technologies that support the diverse needs and experiences of non-binary individuals.

Given the limited availability of gender-neutral pronouns in the training data, how can the model's understanding of non-binary gender identities be further improved?

To enhance the model's understanding of non-binary gender identities despite the limited availability of gender-neutral pronouns in the training data, several strategies can be implemented:

Data Augmentation: Augmenting the training data with synthetic examples that include gender-neutral pronouns can help expose the model to a wider range of pronoun forms and improve its ability to recognize and process non-binary identities.

Fine-tuning with External Data: Incorporating external datasets that contain a diverse set of pronouns, including gender-neutral and neopronouns, can provide the model with additional exposure to different pronoun forms and enhance its understanding of non-binary gender identities.

Multitask Learning: Implementing multitask learning approaches that involve training the model on tasks related to gender identity recognition and pronoun resolution can help reinforce the model's understanding of non-binary gender identities and improve its performance on gender-neutral pronouns.

Regular Evaluation and Feedback: Continuously evaluating the model's performance on pronoun resolution tasks and providing feedback on its predictions can help identify areas of improvement and guide the model towards a more accurate understanding of non-binary gender identities.

By combining these strategies and actively working towards diversifying the training data and exposure to different pronoun forms, the model's understanding of non-binary gender identities can be further improved, even in the absence of abundant gender-neutral pronouns in the training data.