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Course Detail

Course Name Machine Translation and Sequence-to- Sequence Models
Course Code 23AID475
Program B.Tech in Artificial Intelligence and Data Science
Credits 3
Campus Coimbatore , Amritapuri ,Faridabad , Bangaluru, Amaravati

Syllabus

Introduction – Machine Translation Overview – Language Models – Rule based Machine Translation – Statistical Machine Translation – Encoder-decoder models – Attention mechanism – Neural Machine Translation – Phrase based models – Tree based models – Subword level models – Transformer networks – Evaluation metrics

Objectives and Outcomes

Course Objectives

  • The main objective of the course is to obtain basic understanding and implementation skills for modern methods for machine translation
  • This course introduces different approaches to build machine translation systems
  • This course helps the students to understand various evaluation metrics used for assessing the performance of a machine translation model
  • This courses introduces different deep learning architectures used for implementing the machine translation system.

Course Outcomes

After completing this course, students will be able to

CO1

Implement a statistical machine translation system

CO2

Implement a neural machine translation system using RNN-based encoder-decoder architecture

CO3

Implement a neural machine translation system using transformer-based encoder-decoder architecture

CO4

Evaluate the performance of machine translation models

CO-PO Mapping

PO/PSO

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

PSO1

PSO2

PSO3

CO

CO1

2

2

3

2

3

–

–

–

1

–

1

1

3

1

1

CO2

2

2

3

2

3

–

–

–

1

–

1

1

3

1

1

CO3

2

2

3

2

3

–

–

–

1

–

1

1

3

1

1

CO4

–

–

1

–

1

–

–

–

–

–

–

1

–

3

–

Evaluation Pattern

Evaluation Pattern

Assessment

Internal/External

Weightage (%)

Assignments (minimum 2)

Internal

30

Quizzes (minimum 2)

Internal

20

Mid-Term Examination

Internal

20

Term Project/ End Semester Examination

External

30

Text Books / References

Text Books / References

Daniel Jurafsky, James H Martin, Speech & language processing, preparation [cited 2020 June 1] Available from: https://web. stanford. edu/~ jurafsky/slp3 (2018).

Philipp Koehn, Statistical machine translation. Cambridge University Press, 2009..

Philipp Koehn, Neural machine translation. Cambridge University Press, 2020.

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