Machine translation
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Bài giảng Các kỹ thuật học sâu và ứng dụng - Bài 8: Mô hình Sequence-to-Sequence đi sâu vào một kiến trúc quan trọng cho các bài toán chuyển đổi trình tự. Chuyên đề này giới thiệu về dịch máy (Machine Translation), mô hình ngôn ngữ điều kiện và kiến trúc mã hóa-giải mã (Encoder-Decoder Architecture). Mời các bạn cùng tham khảo bài giảng để biết thêm chi tiết!
11p
hoatrongguong03
15-05-2025
0
0
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The study also addresses challenges, such as idiomatic expressions and tool limitations, emphasizing the pivotal role of training programs in addressing issues, educating users, and enhancing tools. In conclusion, the research advocates for an educational shift, urging programs to foster critical thinking.
20p
viling
11-10-2024
3
1
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Mục tiêu nghiên cứu của luận văn là vận dụng kiến thức đã học để xây dựng một hệ thống trả lời tự động, sử dụng mạng học sâu Deep Neural Networks, dựa trên khung làm việc sequence-to-sequence và cơ chế attention để sinh ra câu trả lời tự động từ một chuỗi đầu vào tương ứng. Mô hình được huấn luyện end-to-end GNMT (Google’s Neural Machine Translation) trên tập dữ liệu miền mở có sẵn.
72p
matroinho2510
08-11-2022
80
17
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The purpose of this thesis is to propose two models for using cross-lingual word embedding models to address the above impediment. The first model enhances the quality of the phrase-table in SMT, and the remaining model tackles the unknown word problem in NMT.
14p
tamynhan0
04-07-2020
30
3
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The purpose of this thesis is to propose two models for using cross-lingual word embedding models to address the above impediment. The first model enhances the quality of the phrase-table in SMT, and the remaining model tackles the unknown word problem in NMT.
54p
tamynhan0
04-07-2020
21
6
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Lecture “Natural language processing – Chapter 5: Foundation of statistical machine translation” has contents: Introduction to statistical machine translation, statistical MT systems, three problems in statistical MT systems, translation model, and other contents.
12p
dien_vi01
21-11-2018
25
1
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In the past, we presented tutorials called “Introduction to Statistical Machine Translation”, aimed at people who know little or nothing about the field and want to get acquainted with the basic concepts. This tutorial, by contrast, goes more deeply into selected topics of intense current interest. We aim at two types of participants: 1. People who understand the basic idea of statistical machine translation and want to get a survey of hot-topic current research, in terms that they can understand. 2.
1p
hongphan_1
15-04-2013
67
3
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Semantic Role Labeling (SRL) consists of, given a sentence, detecting basic event structures such as “who” did “what” to “whom”, “when” and “where”. From a linguistic point of view, a key component of the task corresponds to identifying the semantic arguments filling the roles of the sentence predicates. Typical predicate semantic arguments include Agent, Patient, and Instrument, but semantic roles may also be found as adjuncts (e.g., Locative, Temporal, Manner, and Cause).
1p
hongphan_1
15-04-2013
61
2
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We describe Joshua (Li et al., 2009a)1 , an open source toolkit for statistical machine translation. Joshua implements all of the algorithms required for translation via synchronous context free grammars (SCFGs): chart-parsing, n-gram language model integration, beam- and cubepruning, and k-best extraction. The toolkit also implements suffix-array grammar extraction and minimum error rate training. It uses parallel and distributed computing techniques for scalability.
4p
hongphan_1
15-04-2013
51
2
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We developed caitra, a novel tool that aids human translators by (a) making suggestions for sentence completion in an interactive machine translation setting, (b) providing alternative word and phrase translations, and (c) allowing them to postedit machine translation output. The tool uses the Moses decoder, is implemented in Ruby on Rails and C++ and delivered over the web. We are at the beginning of a research program to explore the benefits of these different types of aid to human translators, analyze user interaction behavior, and develop novel types of assistance. ...
4p
hongphan_1
15-04-2013
68
2
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