Investigating the Robustness of Modeling Decisions for Few-Shot Cross-Topic Stance Detection: A Preregistered Study

Core Concepts

Modeling choices such as task definition, architecture, and additional task knowledge significantly impact the performance and robustness of few-shot stance detection models across different datasets and topics.

Abstract

This paper investigates the robustness of different modeling choices for few-shot cross-topic stance detection. The authors preregistered their hypotheses and experiments before conducting the study. The key findings are: The effect of the Same Side Stance (SSSC) definition on performance differs per dataset and is influenced by other modeling choices. There is no clear relationship between the number of training topics and performance. Cross-encoding generally outperforms bi-encoding, but some datasets show the opposite effect. Adding Natural Language Inference (NLI) training to the models gives considerable improvement for some datasets, but inconsistent results for others. The authors conclude that experiments beyond a single dataset or modeling choice are essential for finding robust effects of modeling the concept of 'stance'. They recommend using a diverse set of datasets and systematic modeling experiments when aiming to build robust cross-topic stance detection models for applications like viewpoint-diverse news recommendation.

Stats

The perspectrum dataset with 80-227 training topics achieves the highest performance of F1 = 0.766 with the Pro/Con definition and NLI. The ibmcs dataset with 21-30 training topics achieves F1 = 0.734 with the Pro/Con definition and NLI. The scd dataset with 3-4 training topics achieves the lowest performance of F1 = 0.428 with the Pro/Con definition and NLI.

Quotes

"Some of our hypotheses and the claims in previous literature (Stein et al., 2021) were confirmed by our results, but only in some conditions: Same Side Stance is more cross-topic robust, but considering different datasets and other modelling choices shows that the result is different for different encoding choices." "Cross-encoding generally out-performs bi-encoding, but some datasets show the opposite effect. Adding NLI training to our models gives considerable improvement for some datasets, but inconsistent results for others."

Key Insights Distilled From

Investigating the Robustness of Modelling Decisions for Few-Shot Cross-Topic Stance Detection

by Myrthe Reuve... at arxiv.org 04-08-2024

https://arxiv.org/pdf/2404.03987.pdf

Deeper Inquiries

How can the modeling choices and their interactions be further optimized to achieve more consistent and robust cross-topic stance detection performance across diverse datasets?

To achieve more consistent and robust cross-topic stance detection performance across diverse datasets, several optimization strategies can be implemented: Fine-tuning Strategies: Implement fine-tuning strategies that take into account the specific characteristics of each dataset, such as topic diversity, number of training topics, and dataset size. Fine-tuning the models on a diverse set of topics can help improve generalization and robustness. Ensemble Methods: Utilize ensemble methods to combine the predictions of multiple models trained with different modeling choices. By leveraging the strengths of different models, ensemble methods can help improve overall performance and reduce the impact of individual modeling choices. Transfer Learning: Explore transfer learning techniques to leverage knowledge learned from one dataset to improve performance on another. By transferring knowledge from related tasks or domains, models can adapt more effectively to new topics and datasets. Hyperparameter Optimization: Conduct thorough hyperparameter optimization to fine-tune the model parameters for each dataset. By optimizing hyperparameters such as learning rate, batch size, and model architecture, the model can achieve better performance across diverse datasets. Data Augmentation: Implement data augmentation techniques to increase the diversity of the training data. By generating synthetic data points or introducing variations in the existing data, the model can learn to generalize better to unseen topics and improve robustness. Regularization Techniques: Apply regularization techniques such as dropout, weight decay, or early stopping to prevent overfitting and improve the model's ability to generalize to new topics. Regularization can help reduce the impact of noisy or irrelevant features in the data. Cross-Validation: Use cross-validation techniques to evaluate the model's performance across different subsets of the data. By validating the model on multiple folds of the data, you can assess its robustness and generalization capabilities more effectively. By implementing these optimization strategies and carefully considering the interactions between modeling choices, it is possible to achieve more consistent and robust cross-topic stance detection performance across diverse datasets.

What are the potential biases and limitations of representing human opinions as binary pro/con or same/different stance labels, and how can the models be improved to better capture the nuances and complexities of real-world viewpoints?

Representing human opinions as binary pro/con or same/different stance labels can introduce several biases and limitations: Simplification of Opinions: Binary labels may oversimplify the complexity of human opinions, which are often multifaceted and context-dependent. This simplification can lead to the loss of nuanced information and the inability to capture the full spectrum of viewpoints. Lack of Context: Binary labels may lack the necessary context to fully understand the underlying reasons and motivations behind a particular stance. Without context, models may struggle to accurately capture the nuances and complexities of real-world viewpoints. Bias in Labeling: The process of assigning binary labels to opinions can introduce bias, as annotators may have subjective interpretations or preconceived notions that influence their labeling decisions. This bias can impact the model's ability to generalize to diverse perspectives. To improve the models' ability to capture the nuances and complexities of real-world viewpoints, the following strategies can be implemented: Multi-Class Classification: Instead of binary labels, consider using multi-class classification to allow for a more granular representation of opinions. By defining multiple classes that reflect different shades of opinion, models can better capture the diversity of viewpoints. Fine-Grained Annotation: Use fine-grained annotation techniques to provide more detailed and context-rich labels for training data. Fine-grained annotations can help capture subtle differences in opinions and provide a more comprehensive understanding of human viewpoints. Contextual Understanding: Incorporate contextual information into the modeling process to better understand the background, intent, and reasoning behind each opinion. Contextual understanding can help models interpret opinions in a more nuanced and accurate manner. Adversarial Training: Implement adversarial training techniques to expose the model to diverse and conflicting viewpoints. By training the model to recognize and reconcile conflicting opinions, it can develop a more robust understanding of real-world viewpoints. Human-in-the-Loop Approaches: Involve human annotators or domain experts in the model training process to provide valuable insights, correct biases, and ensure the accurate representation of diverse viewpoints. Human-in-the-loop approaches can help mitigate biases and improve the model's performance. By addressing these biases and limitations and incorporating strategies to capture the nuances and complexities of real-world viewpoints, models can enhance their ability to understand and represent human opinions more accurately.

Given the ethical concerns around the dual-use potential of stance detection models, what additional safeguards or guidelines should be considered when developing and deploying such technologies for applications like viewpoint-diverse news recommendation?

When developing and deploying stance detection models for applications like viewpoint-diverse news recommendation, it is essential to consider the following safeguards and guidelines to address ethical concerns: Transparency and Accountability: Ensure transparency in the development and deployment of stance detection models by clearly documenting the data sources, model architecture, and decision-making processes. Establish accountability mechanisms to track and address any biases or ethical issues that may arise. Ethical Review: Conduct ethical reviews of the model development process to identify and mitigate potential biases, risks, and ethical implications. Involve multidisciplinary teams, including ethicists, domain experts, and stakeholders, in the review process. Bias Detection and Mitigation: Implement bias detection and mitigation techniques to identify and address biases in the data, model, or decision-making processes. Regularly audit the model for biases and take corrective actions to ensure fair and unbiased outcomes. Informed Consent: Obtain informed consent from users when collecting and using their data for training stance detection models. Clearly communicate the purpose of data collection, how the data will be used, and provide users with the option to opt-out or withdraw consent. User Empowerment: Empower users with control over their data and the recommendations they receive. Provide transparency into how stance detection models influence news recommendations and allow users to customize their preferences and filters. Algorithmic Fairness: Prioritize algorithmic fairness by ensuring that stance detection models do not discriminate against individuals or groups based on protected characteristics. Implement fairness-aware algorithms and regular fairness audits to prevent discriminatory outcomes. Human Oversight: Maintain human oversight throughout the model deployment process to monitor the model's performance, intervene in case of errors or biases, and ensure that the recommendations align with ethical standards and societal values. Continuous Monitoring and Evaluation: Continuously monitor and evaluate the performance of stance detection models in real-world settings to identify any emerging ethical concerns or unintended consequences. Regularly update the models based on feedback and new insights. By adhering to these safeguards and guidelines, developers and deployers of stance detection models can mitigate ethical concerns, promote responsible use of technology, and ensure that viewpoint-diverse news recommendation systems uphold ethical standards and societal values.