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Improving Zero-Shot Summarization by Decoupling Language-Specific and Task-Specific Knowledge

Core Concepts
Language-independent representations improve the performance of zero-shot summarization by decoupling task-specific knowledge from language-specific abilities.
The paper focuses on improving zero-shot summarization, both intralingual and crosslingual, by developing methods to decouple language-specific and task-specific knowledge in pretrained multilingual language models. Key highlights: Naive fine-tuning of pretrained models on summarization data leads to highly language-specific representations, resulting in poor zero-shot performance. The authors propose "query-key (QK) finetuning" to selectively update only the query and key projections in the attention modules, while keeping the value projections fixed to retain the pretrained language generation capabilities. For crosslingual zero-shot settings, the authors further introduce a "balanced adversarial language classifier" that encourages the model to learn language-agnostic representations by incentivizing the classifier to predict a uniform distribution over languages. Combining QK finetuning and the balanced adversarial classifier, along with a two-step finetuning approach (first on translation, then on summarization), leads to significant improvements in zero-shot crosslingual summarization performance. Qualitative analyses show that removing source language identity correlates with better zero-shot summarization, confirming the importance of language-agnostic representations.
"Finetuning on English only leads the model to forget its generation ability for other languages, resulting in off-target generation." "Even after explicitly encouraging language-agnostic representations with an adversarial language classifier, recovering the source language identity remains easy."
"Finetuning pretrained models on downstream generation tasks often leads to catastrophic forgetting in zero-shot conditions." "We hypothesize that the value projections should be kept unchanged to prevent losing pretrained generation capabilities during finetuning. In contrast, query and key are updated as adaptation to specific tasks." "A problem with the cross-entropy-based formulation is that it operates on single classes and does not incentivize language-agnostic representations on the output distribution level."

Deeper Inquiries

How would the proposed methods perform on other generation tasks beyond summarization, such as machine translation or text generation

The proposed methods, such as Query-Key (QK) finetuning and the balanced adversarial language classifier, could potentially be applied to other generation tasks beyond summarization, such as machine translation or text generation. For machine translation, the QK finetuning approach could help in retaining language-agnostic representations during the training process, which is crucial for accurate translation across multiple languages. Similarly, the balanced adversarial language classifier could be used to enforce language-agnostic representations in the model, improving the quality of translations across different language pairs. In the case of text generation, these methods could aid in generating diverse and contextually relevant content in multiple languages by ensuring that the model does not rely heavily on language-specific cues.

What other techniques beyond adversarial training could be explored to further improve the language-agnosticity of the learned representations

Beyond adversarial training, several other techniques could be explored to further enhance the language-agnosticity of the learned representations in multilingual models. One approach could involve incorporating explicit constraints or regularization techniques during training to encourage the model to focus more on content rather than language-specific features. For example, incorporating language-agnostic objectives in the loss function, such as minimizing language-specific information in the hidden representations, could help in promoting the development of more language-agnostic representations. Additionally, techniques like multi-task learning with language modeling tasks in multiple languages simultaneously could also contribute to learning more robust and language-independent representations.

How could the insights from this work be applied to improve the multilingual capabilities of large language models during the pretraining stage, rather than relying solely on fine-tuning

The insights from this work could be leveraged to enhance the multilingual capabilities of large language models during the pretraining stage, rather than relying solely on fine-tuning. By incorporating the principles of language-independent representations and zero-shot learning into the pretraining process, it is possible to create models that are inherently more adept at handling diverse languages and tasks. For instance, designing pretraining objectives that explicitly encourage the model to learn language-agnostic representations could lead to more versatile and adaptable models that require minimal fine-tuning for specific tasks or languages. Additionally, incorporating crosslingual generation tasks during pretraining could help in developing models that are inherently capable of performing zero-shot crosslingual generation without catastrophic forgetting.