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DataFunSummit
DataFunSummit
May 23, 2023 · Artificial Intelligence

Continuous Semantic Enhancement for Neural Machine Translation: Methodology, Experiments, and Community Deployment

This article introduces a continuous semantic enhancement approach for neural machine translation that overcomes the limitations of discrete data‑augmentation techniques, details the neighbor risk minimization training objective, presents benchmark improvements on ACL‑2022 datasets, and describes practical deployment and fine‑tuning workflows in the Modu community.

continuous semantic augmentationcontrastive learningdata augmentation
0 likes · 19 min read
Continuous Semantic Enhancement for Neural Machine Translation: Methodology, Experiments, and Community Deployment
DataFunSummit
DataFunSummit
Jan 13, 2022 · Artificial Intelligence

DeltaLM: A Multilingual Pretrained Encoder‑Decoder Model for Neural Machine Translation

DeltaLM is a multilingual pretrained encoder‑decoder model that leverages cross‑lingual transfer from a pretrained encoder and novel decoder architecture, employs span‑corruption and translation‑pair pretraining tasks, and uses a two‑stage fine‑tuning strategy to achieve strong zero‑shot and supervised translation performance across over 100 languages.

DeltaLMZero-shotcross-lingual transfer
0 likes · 12 min read
DeltaLM: A Multilingual Pretrained Encoder‑Decoder Model for Neural Machine Translation
DataFunTalk
DataFunTalk
Apr 7, 2021 · Artificial Intelligence

Alibaba's Advances in Multilingual Neural Machine Translation: Research and Practice

This article presents Alibaba's comprehensive research on multilingual neural machine translation, covering motivations, model architectures, intermediate language modules, data‑augmentation strategies such as repair translation, integration of pre‑trained models with adapters, and engineering optimizations that enable a production‑ready system supporting over 200 languages.

AdapterAlibabaZero-shot
0 likes · 21 min read
Alibaba's Advances in Multilingual Neural Machine Translation: Research and Practice
Sohu Tech Products
Sohu Tech Products
Nov 18, 2020 · Artificial Intelligence

Understanding Sequence‑to‑Sequence (seq2seq) Models and Attention Mechanisms

This article explains the fundamentals of seq2seq neural machine translation models, covering encoder‑decoder architecture, word embeddings, context vectors, RNN processing, and the attention mechanism introduced by Bahdanau and Luong, with visual illustrations and reference links for deeper study.

AttentionRNNdeep learning
0 likes · 11 min read
Understanding Sequence‑to‑Sequence (seq2seq) Models and Attention Mechanisms
Sohu Tech Products
Sohu Tech Products
Jan 9, 2019 · Artificial Intelligence

Understanding the Transformer Model: Attention, Self‑Attention, and Multi‑Head Mechanisms

This article provides a comprehensive, step‑by‑step explanation of the Transformer architecture, covering its encoder‑decoder structure, self‑attention, multi‑head attention, positional encoding, residual connections, and training processes, illustrated with diagrams and code snippets to aid readers new to neural machine translation.

Multi-Head AttentionPositional EncodingSelf-Attention
0 likes · 16 min read
Understanding the Transformer Model: Attention, Self‑Attention, and Multi‑Head Mechanisms