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DA-Net: Disentangled and Adaptive Network for Multi-Source Cross-Lingual Transfer Learning

Core Concepts
The author proposes DA-Net to address challenges in multi-source cross-lingual transfer learning by introducing a Disentangled and Adaptive Network. The approach aims to purify input representations and align class-level distributions for improved model performance.
DA-Net introduces innovative methods, including Feedback-guided Collaborative Disentanglement (FCD) and Class-aware Parallel Adaptation (CPA), to enhance multi-source cross-lingual transfer learning. Experimental results demonstrate the effectiveness of DA-Net in improving adaptation across languages and mitigating interference from multiple sources. Key points: Multi-source cross-lingual transfer learning aims to transfer knowledge from labeled source languages to an unlabeled target language. Existing methods face challenges due to shared encoders containing information from different source languages. DA-Net proposes FCD to purify input representations and CPA to align class-level distributions, improving model performance. Experimental results on NER, RRC, and TEP tasks involving 38 languages validate the effectiveness of DA-Net.
81.33 83.99 82.83 85.57 56.93 38.69 49.45 60.93 66.47 72.82 36.35 78.81 68.84 83.13 81.55 68.51 80.41 82.26 83.99 83.49 55.00 46.54 51.35 63.34 64.04 70...
"No annotation in the target language makes class-wise alignment challenging." "DA-Net's FCD method helps purify input representations, reducing interference among sources." "The CPA method bridges the language gap between source-target pairs for improved adaptation."

Key Insights Distilled From

by Ling Ge,Chun... at 03-08-2024

Deeper Inquiries

How can the concept of disentanglement be applied in other areas of machine learning

Disentanglement can be applied in various areas of machine learning to improve model performance and interpretability. In computer vision, disentangled representations can help separate factors like shape, color, and texture, leading to better understanding of image features. This can aid in tasks such as object detection, segmentation, and image generation. In natural language processing, disentanglement can assist in separating content from style or sentiment in text data. This separation could be beneficial for tasks like sentiment analysis, text summarization, or authorship attribution.

What are potential limitations or drawbacks of using multiple source languages in cross-lingual transfer learning

Using multiple source languages in cross-lingual transfer learning may introduce certain limitations or drawbacks: Increased Complexity: Working with multiple source languages adds complexity to the model architecture and training process. Language Heterogeneity: Different source languages may have varying linguistic structures and characteristics that could make it challenging to align them effectively. Data Imbalance: Source languages might not have equal amounts of labeled data available for training the model, leading to imbalanced representation across languages. Interference between Languages: The shared encoder used by all sources may lead to interference between different language representations if not properly managed.

How might the findings of this study impact the development of multilingual models beyond named entity recognition tasks

The findings of this study could have significant implications for the development of multilingual models beyond named entity recognition tasks: Improved Generalization: The proposed methods (FCD and CPA) show promising results in enhancing generalization capabilities across multiple source-target language pairs. Enhanced Adaptation: By addressing issues related to mutual interference among sources and bridging language gaps through class-level alignment, these techniques could enhance adaptation performance on a broader range of multilingual tasks. Model Robustness: Implementing collaborative disentanglement and parallel adaptation strategies could potentially improve the robustness of multilingual models when dealing with diverse linguistic contexts. Transferability Across Tasks: The principles behind FCD and CPA methods might be transferable to other cross-lingual tasks beyond NER, enabling more effective knowledge transfer between different languages for various NLP applications such as machine translation or sentiment analysis.