Summary of Mosecrot: Model Stitching with Static Word Embeddings For Crosslingual Zero-shot Transfer, by Haotian Ye et al.
MoSECroT: Model Stitching with Static Word Embeddings for Crosslingual Zero-shot Transfer
by Haotian Ye, Yihong Liu, Chunlan Ma, Hinrich Schütze
First submitted to arxiv on: 9 Jan 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 paper introduces a novel task called MoSECroT (Model Stitching with Static Word Embeddings for Crosslingual Zero-shot Transfer) that aims to facilitate zero-shot transfer of pre-trained language models (PLMs) to low-resource languages. The proposed framework leverages relative representations to construct a common space between the PLM’s source language and the target language’s static word embeddings, enabling the training of the PLM on source-language data and direct transfer to the target language. However, experiments on two classification datasets show that while the framework is competitive with weak baselines for MoSECroT, it fails to achieve competitive results compared to strong baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about how to make language models work across languages. It’s like having a special tool that can translate words from one language to another without needing much training data. The researchers tried this approach on two different tasks and found that while it works okay, it’s not as good as some other methods that require more data. They’re trying to figure out why it didn’t work better and how they can improve it. |
Keywords
* Artificial intelligence * Classification * Zero shot
