Summary of Semi-supervised Learning For Bilingual Lexicon Induction, by Paul Garnier and Gauthier Guinet
Semi-Supervised Learning for Bilingual Lexicon Induction
by Paul Garnier, Gauthier Guinet
First submitted to arxiv on: 10 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 This paper proposes a novel approach to unsupervised bilingual induction, which enables the inference of a bilingual lexicon without parallel data. Building on recent work in word embedding alignment, the authors ask whether it’s possible to integrate knowledge from multiple languages when learning a new one. They formulate this problem as a ranking task and propose a semi-supervised framework that leverages corpora from multiple idioms. Experimental results on standard benchmarks show that their approach outperforms state-of-the-art methods in inferring dictionaries for more than 20 languages. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to teach machines to learn new languages without needing a direct translation guide. It’s like trying to remember words in another language by comparing them to similar words in a language you already know. The researchers created a new way to do this, using tools from machine learning to rank the similarity between words across different languages. This approach works well for many languages and can even help us understand more about how languages are related. |
Keywords
* Artificial intelligence * Alignment * Embedding * Inference * Machine learning * Semi supervised * Translation * Unsupervised
