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The Situation
You are a member of a lab group of undergraduate researchers at UCSC studying interesting and relevant topics in contemporary digital discourse (see below for descriptions). Your lab group is looking for new projects to take on this academic year, but first it needs to know what research has already been done on the questions it is interested in. Your task is to create, for the members of your research lab group, a literature review, a document in a genre whose main purpose is to synthesizes current research on a topic or question. Literature reviews serve an important function in knowledge-building activities in academic discourse communities.

Your aim is to provide your chosen discourse community (your lab group members) with a clear sense of the state of existing research on a topic, the significance of this body of research, and the opportunities that exist to further the goals of this research. Each member of the lab group will create their own research question that is related to the themes of the lab group and will write their own document with the other members of the lab group as their audience. 

Your job is not to make an argument about how you feel about the research. In fact, try not to think in terms of “sides,” or “opinion,” or “persuasion.” Do not write for a debate because most academic conversations aren’t really “debates.” Think in terms of offering something comprehensive and significant to the discourse community about the questions that matter to it and to you. 

Specifications

• 1250-1500 polished words in a style that is appropriate for the intended academic discourse community
• 5-7 sources with at least 3 academic/scholarly sources. News sources and gray literature sources are appropriate, but background sources are not. 
• include data visualizations, diagrams, and images as needed and properly cited in APA style
• APA citation style and document formatting (Links to an external site.)

Can Artificial Intelligence Change According To The Language Environment? If So, What Are The Advantages Or Disadvantages?

Botian Liu (#1721713)

University of Santa Cruz

WRIT 2-22

Dr. Philip Longo

November 25,2020

Abstract

I chose it because I believe that in the 21st century, artificial intelligence has gradually entered our lives and is closely connected. Language-communication is a behavior that people need every day, but it is very difficult for machines to communicate with us by writing programs, and the diversity of language environments around the world makes this idea even more difficult. I will focus on the research of artificial intelligence on Chinese language. As far as I know, the language environment in China is quite rich, even though the common language is Mandarin. However, due to different regions, China has produced ten different dialects. Because of the different pronunciations, artificial intelligence has also encountered difficulties in mastering these dialects. Because the goal of our team is how artificial intelligence accepts and masters our language, the current understanding and use of different languages by robots has indeed become a problem. (For example, each country has different rhetoric techniques, for example, the same word in China has different meanings due to different intonations).

Keyword：Artificial Intelligence，Chinese Language.

Chinese complex usage

Every country in the world has its own language, such as English, French, Russian and Chinese as we know it. English is divided into two parts, namely American pronunciation and British pronunciation. Among them, the British pronunciation is more powerful, such as “car”, the British pronunciation is [ka:], and the American pronunciation is [kar]. Russian and Chinese are even more different. Russian tongue sounds are very serious, while Chinese requires continuous tongue curling to determine the pronunciation. Since the research direction of this article is Chinese, I will focus on the usage of Chinese vocabulary.

Regarding Chinese, I think everyone will know something about it. Many Americans have one or two Chinese words, such as “nihao”, which means hello. This is not a difficult vocabulary, and you can learn it without excessively rolling your tongue. But “erduo” or “ear” is a typical tongue-rolling pronunciation. But the difficulty of learning Chinese does not stop at this point. In the book “Chinese Lexical Semantics”, it is written that the word “zenme” is a common word in Chinese sentences. It has many meanings, such as “how”, ” why”. Next I will explain their usage. From Ben Liu (Department of Chinese Language & Literature, Peking University, 2015，p74).

“1. 他 怎么 学会 广州话 的？

he how master Cantonese DE?

How did he master Cantonese?

2. 这 事 我 该 怎么 跟 他 说？

this I should how to he say?

How should I tell him about this?

”

As you can see, the above sentence indicates that “zenme” cannot be used before the verb. It is relatively rigid, but the following ions will be used in another way. From Ben Liu (Department of Chinese Language & Literature, Peking University, 2015，p75).

“1. 他 怎么 这么 高兴？

he why so happy?

Why was he so happy?’

2. 小李 怎么 没 报名？

XiaoLi why NEG sign up?

Why didn’t XiaoLi sign up?’

3. 怎么 他 还 不 出来？

why he yet NEG come out?

Why hasn’t he come out yet?”

From these sentences, you can see that “zenme” can be used in front of verbs or adjectives. You should already know that “zenme” in the second sentence is more semantically flexible than the first example. The first example can be used In the future tense and the past tense, but in the second example “zenme” is usually only used in the past tense.

The use of artificial intelligence in a multilingual environment

·In the 21st century, artificial intelligence has been widely used in various fields. Google translation, which is often used around us, is a good example. It can use the database created by programmers to match multiple languages to achieve different languages. The targeted translation of “AI still doesn’t have the common sense to understand human language” written by Karen Hao (2020) mentioned that effective progress has been made in the field of natural-language processing (NLP), but only through the database The update to match the language, this is not a real sense of mastering a multi-language environment, it still requires human operation.

To truly master the multilingual environment, robots must be required to understand the semantic and logical relationships in different languages. Regarding the logical problems of multilingual environments, we can find clues in the article “Quantifiers in Natural Languages: Some Logical Problems, I” written by Jaakko Hibkikka. The author points out that artificial intelligence often misunderstands the correct meaning of sentences when performing semantic analysis. In these misunderstood sentences, most of the reasons come from the logic of the sentence. We can understand it in this way, comparing language to symbols. When the robot masters the symbols, they also know what the symbols express. The most important feature of symbols is subjectivity or arbitrariness. For example, we often say that roses represent love. In fact, there is no inevitable connection between roses and love (the natural attributes of roses do not include representing love). Subjective logic gives this connection and makes it a logical idea for the entire society.

An important branch in the field of artificial intelligence, Natural Language Processing (NLP). NLP starts from the perspective of computer science and is considered a sub-discipline of computers. The purpose of NLP is to efficiently process natural language algorithms. For example, Chinese word segmentation based on character sequence annotation, Among them, Multi-task learning (MTL) is the mainstream force in the NLP language model. NLP in big data computing has achieved great results, but it relies heavily on artificial means, and in contrast, there is much less in-depth linguistic thinking.

The danger of artificial intelligence

Or it has warned the world that the rapid development of artificial intelligence will affect the world. In 2020, we will become accustomed to artificial intelligence around us, siri, smart homes, and smart replies on web pages. Although the artificial intelligence in science fiction movies is out of control and super high IQ is still a long way away, with the development of technology, this is not impossible. A very simple example, you ask Siri if you can bbox, siri will perform a singing session, some netizens found that this phenomenon did not start with a system update, or that the robot has been completed by the self-upgrading code written for them by the researcher Self-upgrading? The most famous example is that machine learning experts from the British company Swiftkey created a learning program. This technology has been used as a smart phone keyboard application, which can learn the user’s ideas and suggest the next word.

Work Sited

Chinese Lexical Semantics，16th Workshop, CLSW 2015, Beijing, China, May 9-11, 2015,p74. by Qin Lu ，Helena Hong Gao（2015）

https://link-springer-com.oca.ucsc.edu/book/10.1007%2F978-3-319-27194-1?page=2#toc

Cécile L. Paris，

（1991）

Natural Language Generation in Artificial Intelligence and Computational Linguistics.by Cécile L. Paris，William R. Swartout，William C. Mann

（1991）

https://link.springer.com/book/10.1007/978-1-4757-5945-7#about

Martin Charles Golumbic （1990）Advances in Artificial Intelligence. By Martin Charles Golumbic （1990）

https://link.springer.com/book/10.1007/978-1-4613-9052-7#about

“AI still doesn’t have the common sense to understand human language” by Karen Hao on January 31,2020.

https://www.technologyreview.com/2020/01/31/304844/ai-common-sense-reads-human-language-ai2/

“Stephen Hawking warns artificial intelligence could end mankind” by Rory Cellan-Jones

（2014）

https://www.bbc.com/news/technology-30290540

“Quantifiers in Natural Languages: Some Logical Problems, I” by Jaakko Hintikka

（1977）

https://link.springer.com/chapter/10.1007/978-94-009-2727-8_9

”

Introduction

With the development of big data, cloud computing and artificial intelligence, many technological innovations have emerged, and people’s life has become more and more convenient. Machine translation is one of the most important technologies. It refers to the automation technology that can translate oral or written text from one language to another without human participation. As the Internet has opened up a wider multilingual world for people, this language service has become very valuable.In the past few years, machine translation research and development is amazing. Back in 2016, Google Translate launched neural machine translation, and it uses phrase based machine translation in order to reduce the gap between human translation and machine translation. This review will focus on the two question based on the development of Google Translate: 1)how does Google Translate work? 2)would it replaces the human translation?

Machine Translation: Google Translate

In terms of the nature of machine translation, it is a translation method that will automatically rely on computer technology and information technology, also the translation principles play an important role. The methods of machine translation include the literal translation method, the conversion method achieved through cohesion and conversion, and the intermediate language method that achieves the purpose of translation through feedback from the intermediate language. The principle of these three methods is to analyze and study the language in order to achieve the purpose of the translation.Different translation methods have different levels of interpretation.

As for literal translation, it is a kind of translation method which is more direct and does not need to analyze and study the language, which is the most common method for machine translation. It belongs to a kind of translation method based on the level of comparison in translation work. The latter two methods need to analyze and study the language to a certain extent and obtain the translation results on this basis.

Neural Machine Translation(NMT

）

According to the sure-language website, it says Google Translate is mainly based on neural machine translation (NMT).

NMT uses neural network-based technology to achieve more context-accurate translation instead of translating broken sentences one word at a time. Using a large artificial neural network to calculate the probability of a word sequence, NMT puts the complete sentence into an integrated model.

NMT can learn and collect information, aiming to imitate the neurons of the human brain, establish connections, and evaluate input as a whole unit. NMT is analyzed in two stages: encoding and decoding. In the encoding stage, text from the source language is input into the machine and then classified into language vectors. Words that are similar in the context will be placed in a comparable word vector. Next, the decoding stage effectively and seamlessly sends the vector to the target language. Throughout the translation process, technology is more than just translating words and phrases; instead, it is translating context and information.

Figure 1.NMT or SMT: Case Study of a Narrow-domain English-Latvian Post-editing Project

However, the NMT cannot translate 100% accurate either. In the Case Study of SMT and NMT, it shows the NMT even has a higher chance to receive the mistranslation error(Pinnis & Skadiņa, 2017). Which decreases the accuracy of machine translation.

Challenges of Machine Translation

Machine translation is fast, convenient, and has high cost-effectiveness. It can translate large amounts of text in seconds. At the same time, machine translation(for non-informal translate) is free, and there are hundreds of applications that can translate text, images even voice anytime and anywhere with the press of a finger. Not only that, a machine translator can translates hundreds of different languages. This are the highlights of machine translation, which can not achieve by human.

But after this, there are still many weakness in machine translation.

First of all, machine translation is not culturally sensitive. Which means machine cannotg understand or recognize slang, jargon, puns and idioms. There is an example of Google Translate in

Figure 2. Google Translate https://translate.google.com/?ui=tob&sl=auto&tl=en&text=%E4%BA%BA%E5%B1%B1%E4%BA%BA%E6%B5%B7&op=translate

The phrase 人来人往(ren lai ren wang) means prosperous in Chinese, even Google Translate got the literal meaning correct, but it’s hard to interpret the intention based on “People come and go”.

Different cultures have different language systems, and it’s hard for human to be able to program machines to understand or experience a particular culture. Therefore, the translation produced may not conform to the cultural values and specific norms. This is one of the challenges that machines need to overcome.

Secondly, machine translation can not connect words in context. In many languages, the same word may have multiple completely unrelated meanings. In this case, context will have a great impact on the meaning of words, and the understanding of word meaning depends on the context, which is an endless cycle. With current technology, only human beings can combine words with context by determine their actual meaning in sentence. Also, only human beings can translate smoothly in the use of diction. For machine translation, this is undoubtedly very difficult.

Conclusion

Machine translation can replace people to do part of the work in large-scale, fast, and high-quality scenarios. However, it is impossible to achieve high-quality. Fully automatic machine translation can only use in a particularly narrow field. It is unlikely that machine translation will replace humans, at least in the foreseeable future. Especially in some professional fields, it is almost impossible for the current quality of translators to exceed that of high-level translators. Although the effectiveness of human translation is not as good as machine translation, machine translation is not as accurate as human translation. Putting aside the shortcomings of these two, accuracy is the most important thing in translation. If you can’t express the meaning correctly, how long it takes is useless. These two kinds of translation services have their own advantages.

However, we can see that most human translation services now use certain auxiliary translation software.Beside their own advantages, combining the two translations does a win-win strategy. The combination of human and machine not only saves time and guarantees quality, but also conforms to the future development trend.

References

Pinnis, M., & Skadiņa, I. (2017). NMT or SMT: Case Study of a Narrow-domain English-Latvian Post-editing Project [Review].

How does Google Translate work and is it any good? (2019, November 12). Retrieved November 10, 2020, from https://www.sure-languages.com/how-does-google-translate-work-and-is-it-any-good/

Costa-jussa, Marta & Rapp, Reinhard & Lambert, Patrik & Eberle, Kurt & Banchs, Rafael & Babych, Bogdan. (2016). Hybrid Approaches to Machine Translation. 10.1007/978-3-319-21311-8.