다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

《Ｏｘｆｏｒｄ Ａｄｖａｎｃｅｄ Ｌｅａｒｎｅｒ’ｓ Ｄｉｃｔｉｏｎａｒｙ ｏｆ Ｃｕｒｒｅｎｔ Ｅｎｇｌｉｓｈ》（ 以下简称ＯＡＬＤ） 是英语学习词典的先驱。它是第一部既重视词语释义，又重视词法、句法，配有大量例证的词典。我国英语学习者对其第二、三、四版（ 英汉双解版） 都十分熟悉。１９９５ 年，ＯＡＬＤ 第五次修订再版，又有了新的特色，但仍有不足

评《Oxford Advanced Learner’s Dictionary of Current English》第五版

The complete title of this book runs ‘Analyzing Linguistic Data: A Practical Introduction to Statistics using R’ and as such it very well reflects the purpose and spirit of the book. The author guides the reader in about 350 pages from descriptive and basic statistical methods over classification and clustering to (generalised) linear and mixed models. Each of the methods is introduced in the context of concrete linguistic problems and demonstrated on exciting datasets from current research in the language sciences. In line with its practical orientation, the book focuses primarily on using the methods and interpreting the results. This implies that the mathematical treatment of the techniques is held at a minimum if not absent from the book. In return, the reader is provided with very detailed explanations on how to conduct the analyses using R [1]. The first chapter sets the tone being a 20-page introduction to R. For this and all subsequent chapters, the R code is intertwined with the chapter text and the datasets and functions used are conveniently packaged in the languageR package that is available on the Comprehensive R Archive Network (CRAN). With this approach, the author has done an excellent job in enabling researchers and students alike to reproduce the analyses and learn by doing. Another quality as a textbook is the fact that every chapter ends with Workbook sections where the user is invited to exercise his or her analysis skills on supplemental datasets. Full solutions including code, results and comments are given in Appendix A (30 pages). Instructors are therefore very well served by this text, although they might want to balance the book with some more mathematical treatment depending on the target audience. After the introductory chapter on R, the book opens on graphical data exploration. Chapter 3 treats probability distributions and common sampling distributions. Under basic statistical methods (Chapter 4), distribution tests and tests on means and variances are covered. Chapter 5 deals with clustering and classification. Strangely enough, the clustering section has material on PCA, factor analysis, correspondence analysis and includes only one subsection on clustering, devoted notably to hierarchical partitioning methods. The classification part deals with decision trees, discriminant analysis and support vector machines. The regression chapter (Chapter 6) treats linear models, generalised linear models, piecewise linear models and a substantial section on models for lexical richness. The final chapter on mixed models is particularly interesting as it is one of the few text book accounts that introduce the reader to using the (innovative) lme4 package of Douglas Bates which implements linear mixed-effects models. Moreover, the case studies included in this

Analyzing linguistic data: a practical introduction to statistics using R

In translation, considering the document
as a whole can help to resolve ambiguities
and inconsistencies. In this paper, we propose a cross-sentence context-aware approach and investigate the influence of historical contextual information on the performance of neural machine translation
(NMT). First, this history is summarized
in a hierarchical way. We then integrate
the historical representation into NMT in
two strategies: 1) a warm-start of encoder and decoder states, and 2) an auxiliary context source for updating decoder
states. Experimental results on a large
Chinese-English translation task show that
our approach significantly improves upon
a strong attention-based NMT system by
up to +2.1 BLEU points.

/pdf/exploiting-cross-sentence-context-for-neural-machine-4jeg8qokfa.pdf

Exploiting Cross-Sentence Context for Neural Machine Translation

This paper discusses neural machine translation (NMT), a new paradigm in the MT field,
comparing the quality of NMT systems with statistical MT by describing three studies using
automatic and human evaluation methods. Automatic evaluation results presented for NMT
are very promising, however human evaluations show mixed results. We report increases in
fluency but inconsistent results for adequacy and post-editing effort. NMT undoubtedly represents a step forward for the MT field, but one that the community should be careful not to
oversell.

/pdf/is-neural-machine-translation-the-new-state-of-the-art-5d8zltsjlo.pdf

Is Neural Machine Translation the New State of the Art

I Foundations of EBMT.- 1 An Overview of EBMT.- 2 What is Example-Based Machine Translation?.- 3 Example-Based Machine Translation in a Controlled Environment.- 4 EBMT Seen as Case-based Reasoning.- II Run-time Approaches to EBMT.- 5 Formalizing Translation Memory.- 6 EBMT Using DP-Matching Between Word Sequences.- 7 A Hybrid Rule and Example-Based Method for Machine Translation.- 8 EBMT of POS-Tagged Sentences via Inductive Learning.- III Template-Driven EBMT.- 9 Learning Translation Templates from Bilingual Translation Examples.- 10 Clustered Transfer Rule Induction for Example-Based Translation.- 11 Translation Patterns, Linguistic Knowledge and Complexity in EBMT.- 12 Inducing Translation Grammars from Bracketed Alignments.- IV EBMT and Derivation Trees.- 13 Extracting Translation Knowledge from Parallel Corpora.- 14 Finding Translation Patterns from Dependency Structures.- 15 A Best-First Alignment Algorithm for Extraction of Transfer Mappings.- 16 Translating with Examples: The LFG-DOT Models of Translation.

Recent Advances in Example-Based Machine Translation

This paper shows how finite approximations of long distance dependency (LDD) resolution can be obtained automatically for wide-coverage, robust, probabilistic Lexical-Functional Grammar (LFG) resources acquired from treebanks. We extract LFG subcategorisation frames and paths linking LDD reentrancies from f-structures generated automatically for the Penn-II treebank trees and use them in an LDD resolution algorithm to parse new text. Unlike (Collins, 1999; Johnson, 2000), in our approach resolution of LDDs is done at f-structure (attribute-value structure representations of basic predicate-argument or dependency structure) without empty productions, traces and coindexation in CFG parse trees. Currently our best automatically induced grammars achieve 80.97% f-score for f-structures parsing section 23 of the WSJ part of the Penn-II treebank and evaluating against the DCU 1051 and 80.24% against the PARC 700 Dependency Bank (King et al., 2003), performing at the same or a slightly better level than state-of-the-art hand-crafted grammars (Kaplan et al., 2004).

/pdf/long-distance-dependency-resolution-in-automatically-q54aetuqyy.pdf

Long-Distance Dependency Resolution in Automatically Acquired Wide-Coverage PCFG-Based LFG Approximations

A prerequisite for training corpus-based machine translation (MT) systems – either Statistical MT (SMT) or Neural MT (NMT) – is the availability of high-quality parallel data. This is arguably more important today than ever before, as NMT has been shown in many studies to outperform SMT, but mostly when large parallel corpora are available; in cases where data is limited, SMT can still outperform NMT. Recently researchers have shown that back-translating monolingual data can be used to create synthetic parallel corpora, which in turn can be used in combination with authentic parallel data to train a high-quality NMT system. Given that large collections of new parallel text become available only quite rarely, back-translation has become the norm when building state-of-the-art NMT systems, especially in resource-poor scenarios. However, we assert that there are many unknown factors regarding the actual effects of back-translated data on the translation capabilities of an NMT model. Accordingly, in this work we investigate how using back-translated data as a training corpus – both as a separate standalone dataset as well as combined with human-generated parallel data – affects the performance of an NMT model. We use incrementally larger amounts of back-translated data to train a range of NMT systems for German-to-English, and analyse the resulting translation performance.

/pdf/investigating-backtranslation-in-neural-machine-translation-1uvhzp0fai.pdf

Andy Way

Papers

Exploiting Cross-Sentence Context for Neural Machine Translation

Is Neural Machine Translation the New State of the Art

Recent Advances in Example-Based Machine Translation

Long-Distance Dependency Resolution in Automatically Acquired Wide-Coverage PCFG-Based LFG Approximations

Investigating Backtranslation in Neural Machine Translation