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Updated on: 3rd May 2022

TNT501 Human And Machine Translation Quality Assessment Assignment Sample SUSS Singapore

With TNT501, you’ll be able to understand the different models and tools that are used for evaluating the quality of translation. You will explore these frameworks by professional bodies around the world to take into consideration socio-cultural contexts or situations more closely than before. This course will develop your skills in applying these different models and tools to real-life translation projects.

In this course, you’ll explore different models and tools used for evaluating quality in translation. You’ll learn how to apply these frameworks to real-life translation projects, taking into account socio-cultural context and situation. By the end of this course, you’ll be able to confidently use different models and tools to evaluate translation quality.

The training course will equip students with the insights they need to recommend an appropriate strategy for quality assessment. In its end, participants can create models of translation reliability that have objectivity and validity in their design – making them useful tools when working on any given project or program application where accuracy matters most.

This course is designed for translation professionals who want to develop their skills in quality assessment, or for anyone who wants to learn more about how to evaluate the quality of a translation.

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Assignment Activity 1: Critique models of translation quality assessment

There are several models of translation quality assessment that have been proposed over the years. Some of the more popular ones include the Triple-A Approach, the Functional Framework, and the Suitability Theory. Each of these models has its strengths and weaknesses, and it is important to understand them to choose the right one for your needs.

The Triple-A Approach was first introduced by William C.ifacts in 1999. It is based on three factors: adequacy, accuracy, and acceptability. Adequacy refers to whether or not the translation contains all of the information that was present in the original text. Accuracy measures how closely the translation resembles the original text, in terms of both meaning and style. Acceptability looks at whether or not the translation is suitable for its intended audience.

The Functional Framework was developed by Gideon Toury in 1995. It focuses on the functions of a text, such as informative, expressive, or directive. Each function has different requirements, and a translation must be adequate in all of them to be successful.

Assignment Activity 2: Compare human translation with a machine translation

Machine translation is still in its early developmental stages, which is why it’s not as accurate as human translation.

Human translators have years of experience and training in the language they’re translating, which allows them to render an accurate and idiomatic translation. They can also rely on their intuition and cultural knowledge to accurately translate idiomatic expressions or difficult phrases.

Machine translation, on the other hand, relies on software that uses statistical algorithms to compare a sentence in the source language with a sentence in the target language. This process often results in inaccurate translations because it’s unable to take into account all the complexities of human language. For example, machine translation might not be able to distinguish between two words that have similar meanings but are used in different contexts. This can often lead to confusing or incorrect translations.

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Assignment Activity 3: Set up a linguistic, cultural and technical framework for translation quality assessment

To establish a framework for translation quality, it is necessary to consider the various elements that go into making a successful translation. These include linguistic factors, cultural considerations and technical requirements. By taking each of these areas into account, it is possible to create a robust system for assessing translation quality.

Linguistic factors are critical in any discussion of translation quality. The translator must have a good understanding of both the source and target languages to produce an accurate translation. This includes having a strong grasp of grammar, syntax and vocabulary. In addition, the translator must be able to convey the meaning of the original text in the target language, even if that meaning is not literal. Successful translations often require creativity and innovation to convey the same message in a new language.

Assignment Activity 4: Formulate strategies to evaluate human and machine translation

There are different ways to evaluate human and machine translation.

One way is to calculate the translation error rate, which measures how many errors are made in translating a text from one language to another. 

Another way is to use translation quality assessment tools such as the Translation Quality Assessment Kit (TQAK) or the Translation Error Rate Analysis Tool (TERAT). These tools can help assess the quality of a translation by measuring various factors such as accuracy, fluency, and readability. Additionally, human judges can be employed to compare the quality of a machine translation with that of a human translation. 

Finally, it is also possible to use crowdsourcing platforms such as Amazon Mechanical Turk to solicit feedback from many people on the quality of a machine translation.

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Assignment Activity 5: Construct models of translation quality assessment specific to different contexts

There are a variety of ways to assess translation quality, depending on the context in which the translations will be used. Some common measures of translation quality include accuracy, fluency, and readability.

Accuracy is important for ensuring that translations match the source text as closely as possible. Fluency refers to how well a translation reads aloud and sounds natural in the target language. Readability is concerned with how easy a translation is to understand.

Each of these factors can vary depending on the type of text being translated and the intended audience. For example, a legal document must be accurate but may not need to be as fluent or readable as a marketing brochure. A technical manual may require fluency but not readability, while a children’s book may need to be both readable and fluent.

Assignment Activity 6: Appraise the effectiveness of the quality assessment models

The effectiveness of quality assessment models can be appraised by their ability to accurately identify quality problems and their ability to provide a framework for taking corrective actions.

The most common model for assessing quality is the Baldrige Criteria, which focuses on seven areas: leadership, strategic planning, customer focus, measurement and analysis, workforce development, process management, and results. The Criteria are designed to be comprehensive and customizable so that they can be applied in a variety of industries.

The effectiveness of quality assessment models can also be evaluated by their ability to motivate improvement. The Baldrige Criteria include several awards and recognition programs that provide incentives for organizations to improve their quality performance. In addition, many quality assessment models are designed to be used in conjunction with other quality improvement tools and techniques, such as Six Sigma and ISO 9000.

Assignment Activity 7:Improve the translation quality assessment models

These are just a few ways to improve the translation quality and assessment models. What is most important is that the data used is representative of the population and that the focus is on utilizing post-editing and human judgments. Additionally, it is also important to develop new features that can be automatically generated and to improve the machine learning models used for prediction. Finally, it is also essential to incorporate all available resources into one comprehensive system and to continuously iterate and improve upon the current design.

  1. Ensure that the data used is representative of the population.
  2. Develop new features that can be automatically generated.
  3. Improve the machine learning models used for prediction. 
  4. Incorporate all available resources into one comprehensive system.
  5. Continuously iterate and improve upon the current design.

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