Summary of Lcs: a Language Converter Strategy For Zero-shot Neural Machine Translation, by Zengkui Sun et al.
LCS: A Language Converter Strategy for Zero-Shot Neural Machine Translation
by Zengkui Sun, Yijin Liu, Fandong Meng, Jinan Xu, Yufeng Chen, Jie Zhou
First submitted to arxiv on: 5 Jun 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 This paper proposes a novel approach to address the limitations of multilingual neural machine translation models in indicating the desired target language during zero-shot translation. Current strategies rely on language tags (LT) placed at the beginning of source or target sentences, but these LTs are not effective when translating to an unseen language. The authors analyze the effect of LT placement and find that placing it on the decoder side leads to rapid degradation, while placing it on the encoder side results in copying or paraphrasing the source input. To resolve this issue, they introduce the Language Converter Strategy (LCS), which embeds the target language into the top encoder layers, ensuring stable language indication for the decoder. LCS outperforms traditional LT strategies in zero-shot translation, achieving language accuracy up to 95.28% and BLEU scores of 3.07 points higher. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a problem with machine translations that can’t translate to languages it hasn’t seen before. Current methods use labels at the beginning of sentences, but these don’t work well when translating to new languages. The researchers tested different placements for these labels and found that putting them on one side or the other caused problems. They then created a new method called LCS (Language Converter Strategy) that fixes this issue by adding language information into the model’s layers. This new approach works better than old methods, allowing machines to translate with higher accuracy. |
Keywords
» Artificial intelligence » Bleu » Decoder » Encoder » Translation » Zero shot
