Year: 2018
Type: dissertation
Abstract: Machine translation (MT) investigates the use of software to translate a source sentence into the corresponding target sentence.The state-of-the-art MT approach is statistical MT, which learns statistical models from a parallel bilingual corpus to model the translation process.When this thesis started (2015), symbolic MT, which is based on symbolic translation rules, such as phrase-based MT [32], hierarchical phrase-based MT [8], and syntax-based MT [36,43], had the best translation performance.Because symbolic MT is based on context-free and ambiguous symbolic translation rules, a log-linear framework and statistical models learned from parallel or monolingual corpora, such as language models [32], reordering models [63,14], rule selection models [35,10,23], are exploited to select the most possible translation during symbolic MT decoding.Now (2018), neural MT (NMT) [4,55,68], which is based on a single large neural network and does not use any explicit translation rules, has outperformed symbolic MT on various translation tasks.Compared to symbolic MT, NMT uses distributed word representations and generally produces more fluent translations, but often sacrifices adequacy [31], such as NMT is more likely to generate completely unrelated translations, under and over translations.This thesis focuses on hybrid neural-symbolic MT methods, which aim to produce translations that are both fluent and adequate by combining the advantages of both symbolic and neural MT.The first part of our contribution is that we develop various neural models for the log-linear framework of symbolic MT, because neural models learn distributed representations for words and sentences, which can generalize better
DOI: 10.34413/dr.01508
Author: Jingyi Zhang
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3029827299