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He Et Al. (2021).pdf

2021.acl-long.246.pdf
Preview of He et al. (2021)
🔗 Source: aclanthology.org
📊 Size: 535 KB
👤 Author: Qiuxiang He ; Guoping Huang ; Qu Cui ; Li Li ; Lemao Liu
⬇️ Downloads: 88

Summary

Researchers propose a fast and accurate approach to neural machine translation (NMT) using translation memory (TM) within the Transformer framework. The model architecture is simple, using a single bilingual sentence as its TM, and its parameters are optimized through a novel training criterion. Experiments on six TM-specialized tasks and two general tasks show that the proposed approach substantially surpasses strong baselines in terms of BLEU and running time, with gains of up to 4.7 BLEU points on TM-specialized tasks and 0.7-1.0 BLEU points on general tasks. The approach addresses the issue of robustness in TM-based NMT models and makes it possible for a single unified model to handle both translation situations with and without a TM.

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Description

Researchers propose a fast and accurate approach to neural machine translation (NMT) using translation memory (TM) within the Transformer framework.

Technical Information

  • File Format: PDF
  • File Size: 535 KB
  • Pages: 11
  • Language: EN
  • Author: Qiuxiang He ; Guoping Huang ; Qu Cui ; Li Li ; Lemao Liu
  • Total Downloads: 88
  • Last Updated: 6 days ago

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