CS772: Deep Learning for Natural Language Processing
Announcement

The first assignment is uploaded on moodle course page.

The first interaction happened on Friday 8:30 AM (8th January 2021)
Course Details
CS772: Deep Learning for Natural Language Processing
Department of Computer Science and Engineering
Indian Institute of Technology Bombay
Time Table and Venue
 Monday: 9:30 AM to 10:25 AM
 Tuesday: 10:35 AM to 11:30 AM
 Thursday: 11:35 AM to 12:30 PM
Motivation
Deep Learning (DL) is a framework for solving AI problems based on a network of neurons organized in many layers. DL has found heavy use in Natural Language Processing (NLP) too, including problems like machine translation, sentiment and emotion analysis, question answering, information extraction and so on, improving performance on automatic systems by order of magnitude.
The course CS626 (Speech, NLP and the Web) being taught in the first semester in CSE Dept IIT Bombay for last several years creates a strong foundation of NLP covering the whole NLP stack starting from morphology to part of speech tagging, to parsing and discourse and pragmatics. Students of the course which typically number more than 100, acquire a grip on tasks, techniques and linguistics of a plethora of NLP problems.
CS772( Deep Learning for Natural Language Processing) comes as a natural sequel to CS626. Language tasks are examined through the lens of Deep Learning. Foundations and advancements in Deep Learning are taught, integrated with NLP problems. For example, sequence to sequence transformer is covered with application in machine translation. Similarly, various techniques in word embedding are taught with application to text classification, information extraction etc.
CS772 is definitely the need of the hour. While CS626 concentrates on algorithmics and linguistics of NLP, the proposed course will concentrate on Data, Distributions, Neural Models, Nonparametric estimation, Information Coding, Representation and such questions
Course Content
 Background: History of Neural Nets; History of NLP; Basic Mathematical Machinery Linear Algebra, Probability, Information Theory etc.; Basic Linguistic Machinery Phonology, morphology, syntax, semantics
 Introducing Neural Computation: Perceptrons, Feedforward Neural Network and Backpropagation, Recurrent Neural Nets
 Difference between Classical Machine Learning and Deep Learning: Representation Symbolic Representation, Distributed Representation, Compositionality; Parametric and nonparametric learning
 Word Embeddings: Word2Vec (CBOW and Skip Gram), Glove, FastText
 Application of Word Embedding to Shallow Parsing Morphological Processing, Part of Speech Tagging and Chunking
 Sequence to Sequence (seq2seq) Transformation using Deep Learning: LSTMs and Variants, Attention, Transformers
 Deep Neural Net based Language Modeling: XLM, BERT, GPT23 etc; Subword Modeling; Transfer Learning and Multilingual Modeling
 Application of seq2seq in Machine Translation: supervised, semi supervised and unsupervised MT; encoderdecoder and attention in MT; Memory Networks in MT
 Deep Learning and Deep Parsing: Recursive Neural Nets; Neural Constituency Parsing; Neural Dependency Parsing
 Deep Learning and Deep Semantics: Word Embeddings and Word Sense Disambiguation; Semantic Role Labeling with Neural Nets
 Neural Text Classification; Sentiment and Emotion labelling with Deep Neural Nets (DNN); DNN based Question Answering
 The indispensability of DNN in Multimodal NLP; Advanced Problems like Sarcasm, Metaphor, Humour and Fake News Detection using multimodality and DNN
 Natural Language Generation; Extractive and Abstractive Summarization with Neural Nets
 Explainability
References
 Ian Goodfellow, Yoshua Bengio and Aaron Courville, Deep Learning, MIT Press, 2016.
 Dan Jurafsky and James Martin, Speech and Language Processing, 3rd Edition, October 16, 2019.
 Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola, Dive into Deep Learning, ebook, 2020.
 Christopher Manning and Heinrich Schutze, Foundations of Statistical Natural Language Processing, MIT Press, 1999.
 Daniel Graupe, Deep Learning Neural Networks: Design and Case Studies, World Scientific Publishing Co., Inc., 2016.
 Pushpak Bhattacharyya, Machine Translation, CRC Press, 2017.

Journals:Computational Linguistics, Natural Language Engineering, Journal of Machine Learning Research (JMLR), Neural Computation, IEEE Transactions on Neural Networks and Learning Systems

Conferences: Annual Meeting of the Association of Computational Linguistics (ACL), Neural Information Processing (NeuiPS), Intâ€™l Conf on Machine Learning (ICML), Empirical Methods in NLP (EMNLP).
Prerequisites
Data Structures and Algorithms, Python (or similar language) Programming skill