Summary of Mapping Transformer Leveraged Embeddings For Cross-lingual Document Representation, by Tsegaye Misikir Tashu et al.
Mapping Transformer Leveraged Embeddings for Cross-Lingual Document Representation
by Tsegaye Misikir Tashu, Eduard-Raul Kontos, Matthia Sabatelli, Matias Valdenegro-Toro
First submitted to arxiv on: 12 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research aims to bridge the gap in recommendation systems by developing Transformer Leveraged Document Representations (TLDRs) that can represent documents across languages. The study evaluates four multilingual pre-trained transformer models (mBERT, mT5 XLM RoBERTa, ErnieM) using three mapping methods on 20 language pairs from five European Union languages. The results demonstrate the effectiveness of cross-lingual representations achieved through pre-trained transformers and mapping approaches, suggesting a promising direction for expanding beyond language connections. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem in how we find relevant documents online. Right now, recommendation systems often can’t find documents in languages other than the one you’re searching in. That’s like looking for recipes in French when your search is in English! This research creates a way to represent documents across different languages using powerful AI models called transformers. They test four of these models on 20 language pairs from European Union countries and show that this approach works really well. |
Keywords
* Artificial intelligence * Transformer
