Neural Machine Translation with Soft Prototype

Volume: 32, Pages: 6313 - 6322
Published: Jan 1, 2019
Abstract
Neural machine translation models usually use the encoder-decoder framework and generate translation from left to right (or right to left) without fully utilizing the target-side global information. A few recent approaches seek to exploit the global information through two-pass decoding, yet have limitations in translation quality and model efficiency. In this work, we propose a new framework that introduces a soft prototype into the...
Paper Details
Title
Neural Machine Translation with Soft Prototype
Published Date
Jan 1, 2019
Volume
32
Pages
6313 - 6322
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