Summary of Language-independent Representations Improve Zero-shot Summarization, by Vladimir Solovyev et al.
Language-Independent Representations Improve Zero-Shot Summarization
by Vladimir Solovyev, Danni Liu, Jan Niehues
First submitted to arxiv on: 8 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed method aims to mitigate catastrophic forgetting in zero-shot generation tasks by fine-tuning pre-trained models on monolingual summarization and then performing transfer learning to new languages or language pairs. The study reveals that naively finetuned models are highly language-specific, leading to poor performance in zero-shot conditions. To address this issue, the authors propose query-key (QK) finetuning to decouple task-specific knowledge from pre-trained language generation abilities. Additionally, a balanced variant of an adversarial language classifier is proposed to enforce language-agnostic representations. The results show that removing source language identity correlates with improved zero-shot summarization performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a big problem in artificial intelligence. Right now, when we teach machines to generate text in one language, they often forget how to do it in other languages. This is bad because we want our AI systems to be able to communicate across languages. The researchers propose a new way to train these models that keeps their ability to understand and generate text in many languages. They test this approach on summarization tasks and show that it works well, even when the model hasn’t seen the language before. |
Keywords
» Artificial intelligence » Fine tuning » Summarization » Transfer learning » Zero shot
