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EBBS: An Ensemble with Bi-Level Beam Search for Zero-Shot Machine Translation

Core Concepts
EBBS is a novel ensemble method with a bi-level beam search algorithm that outperforms direct and pivot translations, improving translation quality in zero-shot machine translation.
EBBS introduces a unique approach to zero-shot machine translation by utilizing an ensemble method with a bi-level beam search algorithm. The study focuses on multilingual translation in the zero-shot setting, where traditional methods like direct and pivot translations have limitations due to noise and error accumulation. EBBS synchronizes individual ensemble components through a "soft voting" mechanism, allowing for high-quality output text despite lacking parallel data. Experimental results on popular multilingual datasets show that EBBS consistently outperforms existing ensemble techniques, demonstrating its effectiveness in improving translation quality and efficiency.
Results show that EBBS consistently outperforms direct and pivot translations. EBBS achieves higher performance in 56 out of 62 cases across two datasets. The proposed ensemble method improves inference efficiency without sacrificing translation quality.
"The ability of zero-shot translation emerges when we train a multilingual model with certain translation directions; the model can then directly translate in unseen directions." "We propose EBBS, an ensemble method with a novel bi-level beam search algorithm, where each ensemble component explores its own prediction step by step at the lower level but they are synchronized by a “soft voting” mechanism at the upper level." "Results on two popular multilingual translation datasets show that EBBS consistently outperforms direct and pivot translations as well as existing ensemble techniques."

Key Insights Distilled From

by Yuqiao Wen,B... at 03-04-2024

Deeper Inquiries

How can the concept of soft voting be applied to other areas of machine learning beyond machine translation


What are potential drawbacks or limitations of using an ensemble approach like EBBS in real-world applications


How might advancements in neural network architectures impact the effectiveness of bi-level beam search algorithms like EBBS

ニューラルネットワークアーキテクチャーの進歩はEBBSなどバイレベルビームサーチアルゴリズムの有効性に影響します。例えば、「Transformer」など新しいニューラルネットワーク構造では長期依存関係処理能力やパラメータ効率化など改善された特徴を持っています。これら進歩したニューラルネットワークはより複雑なパターン認識能力と柔軟性を提供し,バイレベ ルビームサ ージ ア ル ゴ リ ズ ム の 性 能 向 上 お よ び 拡 張 を 可 能 と す る 傾 向 です 。このような進展は EBBS の精度向上だけでなく,処理速度・計算負荷削減・多言語対応拡張等でも大きく貢献しうる可能性も示唆しています。