Evaluating the Impact of Data Partitioning Strategies on Model Generalizability: A Cross-Linguistic Study of Morphological Segmentation

Core Concepts

Different data partitioning strategies, such as random and adversarial splits, can significantly impact the generalizability of computational models for morphological segmentation across diverse languages.

Abstract

This study investigates the effect of data partitioning strategies on model generalizability for the task of morphological segmentation, using data from 19 typologically diverse languages, including 10 indigenous/endangered languages. The key findings are: When facing new test data, models trained using random splits of the data generally achieve higher numerical scores and exhibit more consistent model rankings compared to those trained using adversarial splits. Regression analysis confirms that random splits have a significantly positive effect on model performance for most languages, regardless of their morphological properties or data availability. The advantage of random splits over adversarial splits is more pronounced when the new test data is generated adversarially, posing greater challenges relative to the training data. The authors conclude that random data partitioning is a more reliable strategy for evaluating model generalizability, at least in the context of morphological segmentation. This finding has important implications for conducting robust model evaluations, especially for low-resource and endangered languages.

Stats

Models trained on random splits achieve higher F1 scores on new test samples compared to models trained on adversarial splits. The average F1 score difference between random and adversarial splits is larger when the new test samples are generated adversarially. Regression analysis shows that random splits have a significantly positive effect on model performance for 16 out of 19 languages examined.

Quotes

"Random splits, in contrast to adversarial splits, yield: (1) better model performance, and (2) more reliable model rankings on new test data." "When facing more challenging new test data in the wild (challenging relative to the training data), there is potentially more benefit in applying random splits, at least in the case of morphological segmentation."

Key Insights Distilled From

The Effect of Data Partitioning Strategy on Model Generalizability: A Case Study of Morphological Segmentation

by Zoey Liu,Bon... at arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.09371.pdf

Deeper Inquiries

How would the findings change if the new test data were drawn from a different domain rather than the same domain as the training data

If the new test data were drawn from a different domain rather than the same domain as the training data, the findings of the study on data partitioning strategies for model generalizability in morphological segmentation could potentially change. When the new test data comes from a different domain, the effectiveness of the data partitioning strategies may vary. Random splits, which showed better model performance and more reliable model rankings on new test samples in the study, might not be as effective when faced with new test data from a different domain. Adversarial splits, which aim to create test data that is as different as possible from the training data, could potentially perform better in such scenarios by providing a more robust evaluation of model generalizability. The impact of domain shift on model generalizability is crucial to consider in real-world applications where models need to perform well on unseen data from diverse sources. Adapting data partitioning strategies to account for domain differences can help improve the robustness and reliability of natural language processing models.

What other factors, beyond data partitioning strategies, could influence model generalizability for morphological segmentation across diverse languages

Beyond data partitioning strategies, several other factors could influence model generalizability for morphological segmentation across diverse languages. Some of these factors include: Language Typology: The morphological complexity and typological traits of languages can significantly impact model performance. Languages with different morphological systems (e.g., polysynthetic, fusional, agglutinative) may require tailored approaches for effective segmentation. Data Availability: The amount and quality of training data available for each language can affect model generalizability. Languages with limited data may require techniques like data augmentation or transfer learning to improve performance. Model Architecture: The choice of model architecture, such as CRF or neural seq2seq models, can influence how well the model learns and generalizes morphological segmentation tasks. Experimenting with different architectures can help identify the most suitable approach for each language. Feature Engineering: The selection of relevant features for morphological segmentation, such as character n-grams or linguistic features, can impact model performance. Effective feature engineering tailored to the linguistic characteristics of each language is essential. Hyperparameter Tuning: Optimizing model hyperparameters, such as learning rate, batch size, and regularization techniques, can enhance model generalizability. Fine-tuning these parameters for each language can lead to better performance. Cross-Linguistic Transfer Learning: Leveraging knowledge from high-resource languages to improve model performance in low-resource languages through transfer learning can be beneficial. Pre-trained language models and cross-lingual embeddings can aid in transferring linguistic knowledge across languages. Considering these factors alongside data partitioning strategies can provide a comprehensive approach to enhancing model generalizability in morphological segmentation tasks across diverse languages.

How can the insights from this study on data partitioning strategies be applied to improve the robustness of natural language processing models in other tasks and domains

The insights from this study on data partitioning strategies can be applied to improve the robustness of natural language processing models in various tasks and domains by: Task-Specific Evaluation: Adopting multiple data splits and test datasets for model evaluation can provide a more comprehensive assessment of model performance. Applying random splits for model comparison and adversarial splits for challenging new test samples can enhance the reliability of model evaluations. Domain Adaptation: Considering domain shifts in the evaluation process and adapting data partitioning strategies accordingly can improve model generalizability across different domains. Strategies like adversarial splits can help evaluate model performance on diverse data sources. Model Selection: Experimenting with different model architectures and comparing their performance under various data partitioning strategies can guide the selection of the most suitable model for a specific task or language. Understanding how different models rank and perform can inform model selection decisions. Data Augmentation: Generating new test samples with varying sizes and characteristics can simulate real-world scenarios and improve the robustness of models. Augmenting the training data with diverse samples can help models generalize better to unseen data. Hyperparameter Optimization: Fine-tuning model hyperparameters based on the insights gained from data partitioning experiments can optimize model performance. Adjusting hyperparameters for each language or task can lead to improved generalizability. By integrating these insights into the development and evaluation of natural language processing models, researchers and practitioners can enhance the reliability and robustness of models across different tasks and domains.