Summary of Beyond Mle: Investigating Searnn For Low-resourced Neural Machine Translation, by Chris Emezue
Beyond MLE: Investigating SEARNN for Low-Resourced Neural Machine Translation
by Chris Emezue
First submitted to arxiv on: 20 May 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 explores the potential of the Structured Evaluation and Reward Network (SEARNN) in improving machine translation for low-resourced African languages. The authors focus on natural language processing (NLP) applications, where recurrent neural networks (RNNs) are a popular choice. However, traditional Maximum Likelihood Estimation (MLE) training has limitations, including exposure bias and mismatched metrics between training and testing. SEARNN is an alternative framework that can address these challenges. The authors evaluate the efficacy of SEARNN over MLE on machine translation tasks for English to Igbo, French to Ewe, and French to Ghomala directions, achieving an average BLEU score improvement of 5.4%. This demonstrates the viability of SEARNN in training RNNs for low-resourced languages. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to train computers to translate languages that don’t have much data. It’s a big challenge because these languages are complex and it’s hard to get good results. The authors tested this new method, called SEARNN, on three languages: Igbo, Ewe, and Ghomala. They compared it to the traditional way of training computers to translate, which is called MLE. The results show that SEARNN is better than MLE, with an improvement of 5.4%. This means that SEARNN can help us get better at translating these languages. |
Keywords
» Artificial intelligence » Bleu » Likelihood » Natural language processing » Nlp » Translation
