Machine Translation Program’s Usefulness and Continuous Use Intention by the Analysis of the Association Rule

Mosquitoes are vectors of many pathogens which causes serious human diseases like Malaria, Filariasis, Japanese encephalitis, Dengue fever, Chikungunya, Yellow fever and Zika virus which constitute a major public health problem globally. Mosquito borne diseases cause high level of economic impact all over the world and result in millions of death every year. They infect around 700,000,000 people annually worldwide and 40,000,000 only in India. The continuous use of synthetic pesticides to control vector mosquitoes has caused physiological resistance, toxic effect on human health, environmental pollution and addition to these, its adverse effects can be observed on non-target organisms. Synthetic chemical pesticides have been proved to be effective, but overall in last 5 decades indiscriminate use of synthetic pesticides against vector borne disease control have originated several ecological issues due to their residual accumulation and development of resistance in target vectors and their chronic effects.

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A Comparative Study on the Influencing Factors of Continuous Use Intention of Korean and Chinese SNS Users: Focused on the Technology Readiness and Acceptance Model

The Institute of Management and Economy Research ◽

10.32599/apjb.9.4.201812.181 ◽

2018 ◽

Vol 9 (4) ◽

pp. 181-199

Author(s):

Sung-Joon Yoon ◽

Jong-Chul Oh

Keyword(s):

Comparative Study ◽

Influencing Factors ◽

Technology Readiness ◽

Acceptance Model ◽

Continuous Use

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Factored Statistical Machine Translation for German-English

Journal of Applied Information, Communication and Technology ◽

10.33555/ejaict.v5i1.47 ◽

2018 ◽

Vol 5 (1) ◽

pp. 37-45

Author(s):

Darryl Yunus Sulistyan

Keyword(s):

Machine Translation ◽

English Language ◽

Statistical Machine Translation ◽

New Model ◽

Language Pair

Machine Translation is a machine that is going to automatically translate given sentences in a language to other particular language. This paper aims to test the effectiveness of a new model of machine translation which is factored machine translation. We compare the performance of the unfactored system as our baseline compared to the factored model in terms of BLEU score. We test the model in German-English language pair using Europarl corpus. The tools we are using is called MOSES. It is freely downloadable and use. We found, however, that the unfactored model scored over 24 in BLEU and outperforms the factored model which scored below 24 in BLEU for all cases. In terms of words being translated, however, all of factored models outperforms the unfactored model.

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On (Not) Translating Lacan: Barbara Cassin's Sophistico-Analytical Performances

Paragraph ◽

10.3366/para.2020.0323 ◽

2020 ◽

Vol 43 (1) ◽

pp. 98-113

Author(s):

Michael Syrotinski

Keyword(s):

Machine Translation ◽

Reading And Writing ◽

The Relationship ◽

The Way

Barbara Cassin's Jacques the Sophist: Lacan, Logos, and Psychoanalysis, recently translated into English, constitutes an important rereading of Lacan, and a sustained commentary not only on his interpretation of Greek philosophers, notably the Sophists, but more broadly the relationship between psychoanalysis and sophistry. In her study, Cassin draws out the sophistic elements of Lacan's own language, or the way that Lacan ‘philosophistizes’, as she puts it. This article focuses on the relation between Cassin's text and her better-known Dictionary of Untranslatables, and aims to show how and why both ‘untranslatability’ and ‘performativity’ become keys to understanding what this book is not only saying, but also doing. It ends with a series of reflections on machine translation, and how the intersubjective dynamic as theorized by Lacan might open up the possibility of what is here termed a ‘translatorly’ mode of reading and writing.

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A Review and evaluation of Machine Translation methods for Lumasaaba

Journal of Digital Science ◽

10.33847/2686-8296.2.1_1 ◽

2020 ◽

pp. 3-17

Author(s):

Peter Nabende

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Machine Translation ◽

Language Processing ◽

Research Area ◽

Data Driven ◽

East African ◽

Data Set ◽

African Languages ◽

Translation Methods

Natural Language Processing for under-resourced languages is now a mainstream research area. However, there are limited studies on Natural Language Processing applications for many indigenous East African languages. As a contribution to covering the current gap of knowledge, this paper focuses on evaluating the application of well-established machine translation methods for one heavily under-resourced indigenous East African language called Lumasaaba. Specifically, we review the most common machine translation methods in the context of Lumasaaba including both rule-based and data-driven methods. Then we apply a state of the art data-driven machine translation method to learn models for automating translation between Lumasaaba and English using a very limited data set of parallel sentences. Automatic evaluation results show that a transformer-based Neural Machine Translation model architecture leads to consistently better BLEU scores than the recurrent neural network-based models. Moreover, the automatically generated translations can be comprehended to a reasonable extent and are usually associated with the source language input.

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