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In this work, we introduce Language-Specific Transformer Layers (LSLs), which allow us to increase model capacity, while keeping the amount of computation and the number of parameters used in the forward pass constant. The key idea is to have some layers of the encoder be source or target language-specific, while keeping the remaining layers ...
a deep representation by a six-layer encoder, which is subsequently decoded by a six-layer decoder into the translation in the target language. Layers of the encoder and decoder consist of self ...
- Martin Popel, Marketa Tomkova, Jakub Tomek, Łukasz Kaiser, Jakob Uszkoreit, Ondřej Bojar, Zdeněk Žab...
- 2020
Mar 23, 2024 · Figure 1: Applying the Transformer to machine translation. Source: Google AI Blog. That's a lot to digest, the goal of this tutorial is to break it down into easy to understand parts. In this tutorial you will: Prepare the data. Implement necessary components: Positional embeddings. Attention layers. The encoder and decoder. Build & train the ...
Jan 1, 2023 · This article shows a step-by-step implementation of a Multi-lingual Neural Machine Translation (MNMT) model. In this implementation, we build an encoder-decoder architecture-based MNMT. An overview of MNMT: Before diving into the implementation, let’s take a step back and understand what Multi-lingual Neural Machine Translation (MNMT) models ...
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May 6, 2024 · Abstract. Transformer is the state-of-the-art model in recent machine translation evaluations. Two strands of research are promising to improve models of this kind: the first uses wide networks (a.k.a. Transformer-Big) and has been the de facto standard for development of the Transformer system, and the other uses deeper language representation ...
- Qiang Wang, Bei Li, Tong Xiao, Jingbo Zhu, Changliang Li, Derek F. Wong, Lidia S. Chao
- 2019
May 4, 2023 · Abstract: Multilingual Machine Translation promises to improve translation quality between non-English languages. This is advantageous for several reasons, namely lower latency (no need to translate twice), and reduced error cascades (e.g., avoiding losing gender and formality information when translating through English).
Feb 17, 2021 · The authors tested the model on pairings of the languages used in training except English, giving them 134 zero-shot translation tasks. Results: The authors compared their model’s zero-shot translations with those of an unmodified transformer using BLEU, a measure of how well a machine translation matches a reference translation (higher is ...
