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Evaluating Cross-Lingual Transfer Robustness of Multilingual Language Models on Adversarial Datasets in Low-Resource Languages


Core Concepts
Multilingual language models exhibit varying degrees of cross-lingual transfer performance and robustness to adversarial perturbations, depending on the linguistic relationship between the high-resource and low-resource language pairs.
Abstract
The study investigates the cross-lingual transfer capabilities and robustness of two well-known multilingual language models, MBERT and XLM-R, on Named Entity Recognition (NER) and Section Title Prediction tasks across 13 language pairs. The language pairs were selected to have varying degrees of vocabulary overlap due to areal, genetic, or borrowing relationships. The key findings are: There is a strong correlation between the degree of vocabulary overlap between the high-resource language (HRL) and low-resource language (LRL) and the performance of cross-lingual transfer on NER. Perturbing named entities so that the test data contains only non-overlapping words has a significant impact on model performance. While cross-lingual transfer models generally perform worse than native LRL models, they are often more robust to certain types of input perturbations, such as changing context words around named entities. Section title prediction, as a proxy for document classification, appears to rely heavily on word memorization in LRLs, with performance degrading significantly when common words are substituted, even with semantically similar replacements. The results suggest that multilingual models may be encoding biases toward high-resource languages, and that their performance on low-resource languages is sensitive to minor changes in the input, highlighting the need for more equitable consideration of diverse languages in NLP.
Stats
"Perturbing named entities so that the test data contains only non-overlapping words has a statistically very significant impact on model performance." "Cross-lingual transfer models are often somewhat more robust to certain types of perturbations of the input." "Title selection, as a proxy for document classification, in LRLs appears to heavily rely on word memorization."
Quotes
"There is a pronounced effect of vocabulary overlap on NER performance." "Although models utilizing cross-lingual transfer typically exhibit lower numerical performance than models trained in a native LRL setting, they are often somewhat more robust to certain types of perturbations of the input." "Title selection, as a proxy for document classification, in LRLs appears to heavily rely on word memorization."

Deeper Inquiries

How can multilingual language models be further improved to better handle linguistic diversity and reduce biases toward high-resource languages?

Multilingual language models can be enhanced to better handle linguistic diversity and mitigate biases towards high-resource languages through several strategies: Diverse Training Data: Including a more extensive range of languages and dialects in the training data can help the model learn a broader spectrum of linguistic features. This can reduce biases towards high-resource languages by providing more balanced exposure to different language types. Fine-tuning for Low-Resource Languages: Implementing specific fine-tuning techniques for low-resource languages can help the model adapt better to the linguistic nuances and challenges of these languages. This can involve training on more data from these languages or adjusting the model architecture to accommodate their unique characteristics. Bias Mitigation Techniques: Implementing bias detection and mitigation strategies within the model can help reduce biases towards high-resource languages. Techniques such as debiasing algorithms and fairness-aware training can help ensure more equitable performance across different languages. Adversarial Training: Incorporating adversarial training methods can help the model become more robust to biases and adversarial attacks. By exposing the model to a variety of perturbations and challenging scenarios during training, it can learn to generalize better and handle linguistic diversity more effectively. Evaluation Metrics: Developing new evaluation metrics that specifically assess the model's performance on low-resource languages can provide more insights into its capabilities and areas for improvement. These metrics can focus on linguistic diversity, bias detection, and cross-lingual transfer performance.

How might the insights from this study on the relationship between vocabulary overlap and cross-lingual transfer performance inform the development of more efficient and effective multilingual NLP systems for low-resource languages?

The insights from this study can significantly impact the development of multilingual NLP systems for low-resource languages in the following ways: Optimized Training Strategies: Understanding the importance of vocabulary overlap in cross-lingual transfer performance can guide the development of more optimized training strategies. Models can be trained to focus on shared vocabulary and linguistic features between languages to enhance transfer learning capabilities. Robustness Testing: By recognizing the impact of perturbations on model performance, developers can design more robust NLP systems for low-resource languages. Testing models under various adversarial attacks can help identify weaknesses and improve their resilience to input variations. Bias Reduction: Insights into biases towards high-resource languages can lead to the implementation of bias reduction techniques in multilingual models. By addressing these biases, NLP systems can provide more equitable and accurate results for low-resource languages. Fine-tuning Approaches: Tailoring fine-tuning approaches based on the level of vocabulary overlap between languages can enhance the adaptability of models to diverse linguistic contexts. Models can be fine-tuned with a focus on common vocabulary to improve performance in cross-lingual scenarios. Resource Allocation: Understanding the relationship between vocabulary overlap and model performance can help allocate resources more effectively. By prioritizing data collection and model training on languages with significant overlap, developers can maximize the efficiency of multilingual NLP systems for low-resource languages.
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