This course is an introduction to the field of machine translation, including the related and more broad field of computer-aided translation. The course is novel in that it will also involve interdisciplinary learning with materials from and possibly team-based interactions with faculty in students in other, non-technical courses at Villanova. This unique offering affords all involved students and faculty the opportunity to accumulate and apply expertise from their respective disciplines to develop approaches and machine translation tools, much in the way such collaboration is done in academic research and the software industry.

Machine translation in the traditional sense, as a subfield of "computational linguistics," involves use of an automated computer system to convert, or translate, text in one human language to the equivalent text in a different human language. More broadly, the task of machine translation involves using an automated computer system to translate of any form of representation into another form, such as text into speech, speech into text, automatic description of images, cryptography, spam filtering, sentiment analysis, readability analysis, word clouds, or any other form of attempting to understand one representation of knowledge, especially language, in another form.

Machine translation uses computers to turn one form of information into another.

Among the approaches we will study are the three major paradigms of machine translation of language: word-based translation, phrase-based translation, and syntax-based translation. Students will gain hands-on experience with building machine translation systems and working with real-world data, and they will learn how to formulate and investigate research questions in machine translation.

Class meetings and assignments will vary, lecture, video, projects, case studies, hands-on programming, experimentation with software tools, presentations, seminar-style discussions.

The very best of the work done in the semester will be carried forward in collaboration with your instructor, and could lead to submission of research papers to prestigious conferences and journals. Successful publication in conferences may even lead to travel to conferences to present the results of your work.

Above all this course will attempt to be educational, fun, entertaining and meaningful. Class will always be a safe place to explore ideas, devise novel approaches, collaborate with others, make mistakes, solve problems and learn a whole lot while you create some pretty cool pieces of software and even pick up some non-Computer Science knowledge along the way.

The types of projects and activities we will pursue in class are likely to include:

• Learn to program in the Python and Javascript programming languages.
• Create a word-based translator for a language with very simple translation rules, such as Pig Latin, Verlan, Ubbi Dubbi, and Pirate.
• Create more advanced translators for a variety of language translations.
• Evaluate and use online tools that perform various translations, such as Google Translate and the many other similar tools.
• Using the Natural Language Toolkit to create cool tools of all sorts, including phrase- and syntax-based translations, using the Python programming language.
• Create research-oriented machine translation tools to assist students in FRE 1140 to perform a variety of language-related tasks from simple to complex.
• Using Javascript and HTML5 to explore other approaches for creating web-based machine translation tools, such as sentiment analyzers, word clouds, readability analyzers and cryptography tools.
• And  more...

• Outcome 1: Students will demonstrate an understanding of approaches and techniques used in Machine translation through critically evaluating online references, leading and participating in classroom discussions, completing homework assignments, and conducting brief lab activities.
• Outcome 2: Students will apply the knowledge gained to complete a number of programming projects done individually, as part of a small team, and possibly in collaboration with students in a non-technical course.

Objective 2: Provide experience individually and as a team member on an interdisciplinary team in a number of practical tasks including researching topics, evaluating information, designing and conducting experiments, and programming software tools relating to one or more specific aspects of Machine Translation.

• Outcome 1: Students will demonstrate an ability to collaborate with teams to explore, design and experiment.
• Outcome 2: Students with a technical background (e.g. Computer Science) will demonstrate an understanding of software design and implementation of Machine Translation tools for use in applying computer science ideas to the research and analysis needs of topics within a non-technical course.
• Outcome 3: Students with a non-technical background (e.g., a collaborating, non-technical course) will demonstrate an understanding of the use of various Machine Translation software tools and techniques to analyze, understand and possibly solve a variety of environmental issues, and will gain an improved understanding of computing and computational thinking while learning computer science and programming concepts.

25%  Assignments (homework, lab, reading analysis, research tasks)
10%  Midterm exam or project
40%  Projects (individual, team, interdisciplinary and collaborative)
15%  Final project demonstrating overall understanding
10%  Participation (attendance, class discussion, intellectual contribution to class)