Work

Attention Focusing for Neural Machine Translation by Bridging Source and Target Embeddings

Year: 2018

Type: conference-paper

Abstract: In neural machine translation, a source sequence of words is encoded into a vector from which a target sequence is generated in the decoding phase.Differently from statistical machine translation, the associations between source words and their possible target counterparts are not explicitly stored.Source and target words are at the two ends of a long information processing procedure, mediated by hidden states at both the source encoding and the target decoding phases.This makes it possible that a source word is incorrectly translated into a target word that is not any of its admissible equivalent counterparts in the target language.

Source: Proceedings of the conference - Association for Computational Linguistics. Meeting

DOI: 10.18653/v1/p18-1164

Authors: Shaohui Kuang, Junhui Li, António Branco, Weihua Luo, Deyi Xiong

Institutions: Soochow University, University of Lisbon, Alibaba Group (China)

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: National Natural Science Foundation of China

Award: G595882846