A Study on Analysis and Discuss about the Original Sentence in ‘Preservation of the Life’, 『Zhuang Zi』: It May Be Able to Supply All the Fuel, then the Fire Is Transmitted, and We Don’t Know When It Will Come to an End

2020 ◽  
Vol 94 (0) ◽  
pp. 383-416
Author(s):  
Seong Jo Kang
Keyword(s):  
Zhuang Zi ◽  
Original Sentence

The Return of N’Guyen Van Binh

2020 ◽  
Vol 46 (2) ◽  
pp. 25-42
Author(s):  
Geoff Read
Keyword(s):  
Third Republic ◽  
Forced Labor ◽  
Political Prisoner ◽  
Second Empire ◽  
Indentured Servitude ◽  
The Third ◽  
Original Sentence ◽  
French Colonial ◽  
One Year

This article explores the case of N’Guyen Van Binh, a South Vietnamese political prisoner exiled for his alleged role in “Poukhombo’s Rebellion” in Cambodia in 1866. Although Van Binh’s original sentence of exile was reduced to one year in prison he was nonetheless deported and disappeared into the maw of the colonial systems of indentured servitude and forced labor; he likely did not survive the experience. He was thus the victim of injustice and his case reveals the at best haphazard workings of the French colonial bureaucracy during the period of transition from the Second Empire to the Third Republic. While the documentary record is entirely from the perspective of the colonizers, reading between the lines we can also learn something about Van Binh himself including his fierce will to resist his colonial oppressors.

scholarly journals DRr-Net: Dynamic Re-Read Network for Sentence Semantic Matching

2019 ◽  
Vol 33 ◽  
pp. 7442-7449 ◽  
Author(s):  
Kun Zhang ◽  
Guangyi Lv ◽  
Linyuan Wang ◽  
Le Wu ◽  
Enhong Chen ◽  
...  
Keyword(s):  
Natural Language ◽  
Small Region ◽  
Psychological Research ◽  
Attention Mechanism ◽  
Semantic Relations ◽  
Semantic Matching ◽  
Close Attention ◽  
Original Sentence ◽  
Benchmark Datasets ◽  
Sentence Matching

Sentence semantic matching requires an agent to determine the semantic relation between two sentences, which is widely used in various natural language tasks such as Natural Language Inference (NLI) and Paraphrase Identification (PI). Among all matching methods, attention mechanism plays an important role in capturing the semantic relations and properly aligning the elements of two sentences. Previous methods utilized attention mechanism to select important parts of sentences at one time. However, the important parts of the sentence during semantic matching are dynamically changing with the degree of sentence understanding. Selecting the important parts at one time may be insufficient for semantic understanding. To this end, we propose a Dynamic Re-read Network (DRr-Net) approach for sentence semantic matching, which is able to pay close attention to a small region of sentences at each step and re-read the important words for better sentence semantic understanding. To be specific, we first employ Attention Stack-GRU (ASG) unit to model the original sentence repeatedly and preserve all the information from bottom-most word embedding input to up-most recurrent output. Second, we utilize Dynamic Re-read (DRr) unit to pay close attention to one important word at one time with the consideration of learned information and re-read the important words for better sentence semantic understanding. Extensive experiments on three sentence matching benchmark datasets demonstrate that DRr-Net has the ability to model sentence semantic more precisely and significantly improve the performance of sentence semantic matching. In addition, it is very interesting that some of finding in our experiments are consistent with the findings of psychological research.

scholarly journals Dictionary-Guided Editing Networks for Paraphrase Generation

2019 ◽  
Vol 33 ◽  
pp. 6546-6553 ◽  
Author(s):  
Shaohan Huang ◽  
Yu Wu ◽  
Furu Wei ◽  
Zhongzhi Luan
Keyword(s):  
Automatic Evaluation ◽  
Word Level ◽  
Human Evaluation ◽  
Original Sentence ◽  
Novel Approach ◽  
Benchmark Datasets ◽  
Source Sentence ◽  
Input Sentence ◽  
Paraphrase Generation ◽  
Selection Of

An intuitive way for a human to write paraphrase sentences is to replace words or phrases in the original sentence with their corresponding synonyms and make necessary changes to ensure the new sentences are fluent and grammatically correct. We propose a novel approach to modeling the process with dictionary-guided editing networks which effectively conduct rewriting on the source sentence to generate paraphrase sentences. It jointly learns the selection of the appropriate word level and phrase level paraphrase pairs in the context of the original sentence from an off-the-shelf dictionary as well as the generation of fluent natural language sentences. Specifically, the system retrieves a set of word level and phrase level paraphrase pairs derived from the Paraphrase Database (PPDB) for the original sentence, which is used to guide the decision of which the words might be deleted or inserted with the soft attention mechanism under the sequence-to-sequence framework. We conduct experiments on two benchmark datasets for paraphrase generation, namely the MSCOCO and Quora dataset. The automatic evaluation results demonstrate that our dictionary-guided editing networks outperforms the baseline methods. On human evaluation, results indicate that the generated paraphrases are grammatically correct and relevant to the input sentence.

SYNTACTIC TRANSFORMATIONS IN TRANSLATING A SCIENCE ARTICLE

2020 ◽  
pp. 68-80
Author(s):  
O. O. Mykhailenko
Keyword(s):  
English Language ◽  
Professional Competence ◽  
Syntactic Structure ◽  
Target Language ◽  
Sentence Structure ◽  
Highly Qualified ◽  
Scientific Texts ◽  
Original Sentence ◽  
Language Norms ◽  
Science Article

Publishing the research results in a science article with an international professional journal is an optimal way of sharing the information about newest discoveries in the world of science and technology. Not all scientists have a command of English sufficient for writing a science article, in compliance with high language requirements of leading scientific journals. So, the services of highly-qualified translators of scientific texts into English are in great request, and Ukraine is not an exception. Apart from the basic components of translator’s professional competence, especially important is the knowledge of norms of the modern English language scientific discourse. A translator of scientific texts is to have solid knowledge of grammar of source and target languages, regularities in rendering grammar forms and constructions, translation transformations. The largest number of grammar problems in translation is related to understanding the syntactic structure of sentences and a translator’s ability to make necessary transformations. Our research was aimed at analyzing the role of syntactic transformations in reaching the adequacy in English translation of Ukrainian language articles from scientometric journals. The analysis proved that the majority of syntactic transformations were used to bring the source text in conformity with the target language norms. The measure of translation transformations was generally adequate, though there were cases of non-use of syntactic transformations where they were necessary. Grammar literalism was also observed, due to translator’s insufficient understanding of the sentence structure, lack of knowledge of grammar peculiarities of the target language and translation solutions available for solving a particular translation problem. A translator of scientific texts should be particularly attentive to the syntax of the original sentence, analyse it properly, identify grammar phenomena that may cause translation problems and may need syntactic transformations, and build a translated sentence in accordance with the science language norms.

Analyzing Holistic Parsers: Implications for Robust Parsing and Systematicity

2001 ◽  
Vol 13 (5) ◽  
pp. 1137-1170 ◽  
Author(s):  
Edward Kei Shiu Ho ◽  
Lai Wan Chan
Keyword(s):  
Parse Tree ◽  
State Transitions ◽  
Generalization Performance ◽  
Original Sentence ◽  
Representational Space ◽  
Grammatical Errors ◽  
Robust Parsing ◽  
Input Sentence ◽  
Parse Trees ◽  
Tree Representations

Holistic parsers offer a viable alternative to traditional algorithmic parsers. They have good generalization performance and are robust inherently. In a holistic parser, parsing is achieved by mapping the connectionist representation of the input sentence to the connectionist representation of the target parse tree directly. Little prior knowledge of the underlying parsing mechanism thus needs to be assumed. However, it also makes holistic parsing difficult to understand. In this article, an analysis is presented for studying the operations of the confluent pre-order parser (CPP). In the analysis, the CPP is viewed as a dynamical system, and holistic parsing is perceived as a sequence of state transitions through its state-space. The seemingly one-shot parsing mechanism can thus be elucidated as a step-by-step inference process, with the intermediate parsing decisions being reflected by the states visited during parsing. The study serves two purposes. First, it improves our understanding of how grammatical errors are corrected by the CPP. The occurrence of an error in a sentence will cause the CPP to deviate from the normal track that is followed when the original sentence is parsed. But as the remaining terminals are read, the two trajectories will gradually converge until finally the correct parse tree is produced. Second, it reveals that having systematic parse tree representations alone cannot guarantee good generalization performance in holistic parsing. More important, they need to be distributed in certain useful locations of the representational space. Sentences with similar trailing terminals should have their corresponding parse tree representations mapped to nearby locations in the representational space. The study provides concrete evidence that encoding the linearized parse trees as obtained via preorder traversal can satisfy such a requirement.

Sign in / Sign up

Export Citation Format

Share Document