Summary of Asynchronous and Segmented Bidirectional Encoding For Nmt, by Jingpu Yang et al.
Asynchronous and Segmented Bidirectional Encoding for NMT
by Jingpu Yang, Zehua Han, Mengyu Xiang, Helin Wang, Yuxiao Huang, Miao Fang
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 an improved Neural Machine Translation (NMT) model that enhances translation efficiency and quality by leveraging bidirectional contextual information. Building on the Transformer architecture, the proposed model introduces an asynchronous and segmented decoding strategy that processes long sentences more effectively than traditional unidirectional approaches. Experimental results on the IWSLT2017 dataset demonstrate the model’s superiority in accelerating translation and increasing accuracy, particularly for long sentences. The study also analyzes the impact of sentence length on decoding outcomes and explores the model’s performance in various scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making machine language translations better. Right now, computers are good at translating short sentences, but they struggle with longer ones. The researchers created a new way to translate that takes into account both sides of a sentence, not just one side like usual. This makes the translation faster and more accurate, especially for long sentences. They tested this on a special dataset and found it works really well. This could help people communicate better across languages. |
Keywords
* Artificial intelligence * Transformer * Translation
