Measuring Catastrophic Forgetting in Cross-Lingual Classification Transfer Paradigms and Tuning Strategies

מושגי ליבה

Cross-lingual transfer learning can lead to catastrophic forgetting of previously acquired knowledge in the source language. This study compares different cross-lingual transfer strategies and fine-tuning approaches to measure and mitigate this effect.

תקציר

This study investigates the impact of different cross-lingual transfer strategies and fine-tuning approaches on the phenomenon of catastrophic forgetting in language models. The key findings are:

1. Intermediate training (IT), which uses languages sequentially, outperforms cross-lingual validation (CLV), which uses the target language during validation, in terms of cross-lingual transfer performance.

2. However, the CLV strategy better mitigates catastrophic forgetting and retains more knowledge from the source language (English) compared to the IT strategy, especially when performing multiple cross-lingual transfers.

3. The retention of knowledge in English is better with the CLV strategy, while for other languages and across several cross-lingual steps, the IT strategy causes less forgetting.

4. Adapter fine-tuning is more computationally efficient than full model fine-tuning, but the latter performs better overall.

5. The size of the validation set in the CLV strategy significantly impacts the performance of adapter fine-tuning, but has a minimal effect on full model fine-tuning.

The authors provide open-source cross-lingual adapters for multiple tasks in three less-resourced languages, which can be reused by other researchers.

סטטיסטיקה

"The cross-lingual transfer is a promising technique to solve tasks in less-resourced languages."
"LLMs are pre-trained with self-supervised learning where the idea is to learn the data distribution without explicit labels, e.g., models are asked to solve fill-a-gap tasks in natural language settings (Masked Language Modeling (MLM))."
"Conneau and Lample (2019) introduced the task of Translated Language Modelling (TLM), where masked words are predicted in two parallel sentences in different languages, improving the language alignment."

ציטוטים

"When we transfer knowledge for a specific task or a set of tasks from one language to another, we denote the process as cross-lingual transfer."
"A common problem in transfer learning where knowledge is transferred to another problem is catastrophic forgetting (CF) (McCloskey and Cohen, 1989; Kemker et al., 2018) where models forget previously acquired knowledge when the model is adapted to a novel task."

תובנות מפתח מזוקקות מ:

Measuring Catastrophic Forgetting in Cross-Lingual Transfer Paradigms: Exploring Tuning Strategies

by Bosh... ב- arxiv.org 04-16-2024

https://arxiv.org/pdf/2309.06089.pdf

שאלות מעמיקות

How can the proposed cross-lingual transfer strategies be extended to handle more diverse language pairs, including low-resource languages

To extend the proposed cross-lingual transfer strategies to handle more diverse language pairs, including low-resource languages, several approaches can be considered:

Data Augmentation: Augmenting data in low-resource languages can help improve the performance of cross-lingual transfer models. Techniques like back-translation, synthetic data generation, and multilingual pre-training can be utilized to increase the amount of training data available for low-resource languages.

Multilingual Embeddings: Leveraging multilingual word embeddings or contextual embeddings like mBERT, XLM-R, or LASER can help capture language similarities and improve transfer learning across diverse language pairs. These embeddings can provide a shared representation space for different languages, enabling effective knowledge transfer.

Language Alignment Techniques: Implementing techniques for aligning languages in a shared space, such as adversarial training, canonical correlation analysis, or language-specific adapters, can enhance the transferability of models across diverse language pairs. By aligning the representations of different languages, the model can better generalize to unseen languages.

Fine-Tuning Strategies: Exploring different fine-tuning strategies, such as domain adaptation, meta-learning, or few-shot learning, can help adapt models to specific characteristics of low-resource languages. By fine-tuning on target language data, the model can better capture language-specific nuances and improve performance on diverse language pairs.

Transfer Learning Paradigms: Investigating transfer learning paradigms like zero-shot learning, few-shot learning, or meta-learning for cross-lingual transfer can enhance the adaptability of models to new languages. These paradigms can enable models to generalize well with limited data and mitigate the challenges of handling low-resource languages.

By incorporating these approaches, the proposed cross-lingual transfer strategies can be extended to effectively handle more diverse language pairs, including low-resource languages, in a robust and scalable manner.

What other techniques, beyond the ones explored in this study, could be used to further mitigate catastrophic forgetting in cross-lingual transfer learning

Beyond the techniques explored in this study, several additional methods can be employed to further mitigate catastrophic forgetting in cross-lingual transfer learning:

Regularization Techniques: Implementing regularization methods like Elastic Weight Consolidation, L2 regularization, or dropout regularization can help prevent catastrophic forgetting by penalizing large weight updates during fine-tuning. These techniques can stabilize the model's learning process and retain previously acquired knowledge.

Knowledge Distillation: Utilizing knowledge distillation, where a larger pre-trained model transfers knowledge to a smaller model, can help preserve important information during cross-lingual transfer. By distilling knowledge from a teacher model, the student model can learn from multiple languages without forgetting previous knowledge.

Dynamic Weight Adjustment: Employing dynamic weight adjustment mechanisms that prioritize certain parameters or layers during fine-tuning can help maintain the model's stability and prevent catastrophic forgetting. Techniques like selective weight consolidation or dynamic parameter tuning based on importance can be effective in retaining knowledge.

Ensemble Learning: Implementing ensemble learning by combining multiple models trained on different languages can enhance the robustness of cross-lingual transfer models. By aggregating predictions from diverse models, ensemble methods can mitigate forgetting and improve overall performance on a wide range of languages.

Continual Learning Strategies: Exploring continual learning strategies, such as incremental learning, rehearsal techniques, or episodic memory, can enable models to adapt to new languages while retaining knowledge from previous tasks. By incorporating continual learning mechanisms, models can learn sequentially without catastrophic forgetting.

By integrating these additional techniques, the mitigation of catastrophic forgetting in cross-lingual transfer learning can be further enhanced, leading to more stable and adaptable models across diverse languages.

How can the insights from this study on cross-lingual transfer and catastrophic forgetting be applied to other machine learning domains beyond natural language processing

The insights from this study on cross-lingual transfer and catastrophic forgetting can be applied to other machine learning domains beyond natural language processing in the following ways:

Computer Vision: In computer vision tasks, such as object detection, image classification, and segmentation, the concepts of cross-lingual transfer and catastrophic forgetting can be leveraged to improve model generalization across diverse datasets and domains. Techniques like domain adaptation, transfer learning, and regularization can help enhance model performance and robustness in visual recognition tasks.

Speech Recognition: In speech recognition applications, the principles of cross-lingual transfer learning can be utilized to develop multilingual speech models that can recognize and transcribe speech in multiple languages. By adapting models to different language characteristics and mitigating catastrophic forgetting, speech recognition systems can achieve better accuracy and efficiency in diverse linguistic environments.

Reinforcement Learning: In reinforcement learning scenarios, where agents learn to interact with environments to achieve specific goals, the concepts of transfer learning and catastrophic forgetting can be applied to enable agents to adapt to new tasks and environments while retaining knowledge from previous experiences. Techniques like meta-reinforcement learning, policy distillation, and experience replay can facilitate continual learning and knowledge retention in reinforcement learning settings.

By translating the insights and methodologies from cross-lingual transfer and catastrophic forgetting research in natural language processing to other machine learning domains, researchers and practitioners can enhance the adaptability, performance, and stability of models across a wide range of applications and tasks.