Year: Winter Session I 2018-2019
Time: Mon Wed. 13:30 - 15:00pm.
Location: West Mall Swing Space Room 408
Instructor: Dr. Muhammad Abdul-Mageed
Office location: School of Information (iSchool) 494
Office phone: 6048-274-530
Office hours: Mon. 11:00am - 1:00pm or by appointment. (I can also handle inquiries via email or Skype.)
E-mail address: [email protected]
Student Portal: http://canvas.ubc.ca
Rationale/Background: Natural language processing (NLP) is the field focused at teaching computers to understand and generate human language. Dialog systems where the computer interacts with humans, such as the Amazon Echo, constitute an instance of both language understanding and generation, as the machine attempts to identify the meaning of questions and generate meaningful answers. Recent advances in machine learning, especially in Deep learning, a class of machine learning methods inspired by information processing in the human brain, have boosted performance on several NLP tasks.
Deep learning of natural language is in its infancy, with expected breakthroughs ahead. Solving NLP problems directly contributes to the development of pervasive technologies with significant social and economic impacts and the potential to enhance the lives of millions of people. Given the central role that language plays in our lives, advances in deep learning of natural language have implications across almost all fields of science and technology, as well as many other disciplines like linguistics, as NLP and deep learning are instrumental for making sense of the ever-growing data collected in these fields.
Goal: This course provides a graduate-level introduction to Natural Language Processing With Deep Learning. The goal of the course is to familiarize students with the major NLP problems and the primary deep learning methods being developed to solve them. This includes problems at various linguistic levels (e.g., word and sub-word, phrase, clause, and discourse). Methodologically, this involves unsupervised, distributed representations and supervised deep learning methods across these linguistic levels. The course also includes providing an introductory level of hands-on experience in using deep learning software as well as opportunities to develop advanced solutions for NLP problems in the context of a final project.
Potential audiences for this course are:
- Students with a linguistics and/or a computer science background interested in learning novel NLP methods, with a focus on deep learning.
- People interested in NLP, text analytics, information retrieval, information visualization, human-information interaction, machine learning and deep learning, etc., who want to prepare for, or advance, carrying out research in these fields exploiting deep learning of natural language methods.
Upon completion of this course students will be able to:
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identify the inherent ambiguity in natural language, and appreciate challenges associated with teaching machines to understand and generate it
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become aware of a core of NLP problems, and demonstrate how these are relevant to the lives of diverse individuals, communities and organizations.
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become aware of the major deep learning methods being developed for solving NLP problems, and be in a position to apply this deepened understanding in critical, creative, and novel ways
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collaborate effectively with peers through course assignments
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identify an NLP problem (existing or novel) and apply deep learning methods to develop a solution for it
- ambiguity in human language (across different levels) % week 01
- learning words & phrases (word vectors)% weeks 2, 3%, and sentences
- recurrent neural networks
- convolutional neural networks
- seq2seq models
- deep generative models (auo-encoders and generative adversarial networks)
- POS tagging and morphological disambiguation
- learning in low-resource languages
- language/dialect identification
- emotion and sentiment analysis
- natural language inference
- machine translation
- have some familiarity with at least one linguistics field
- have some familiarity with basic linear algebra, basic calculus, and basic probability (basic = high school level)
- have programming experience in Python
- have access to a computer (preferably with a GPU) on a regular basis
Students lacking any of the above pre-requisites must be open to learn outside their comfort zone to make up, including investing time outside class learning these pre-requisites on their own. Some relevant material across these pre-requisites will be provided as part of the syllabus. Should you have questions about pre-requisites, please email the instructor.
• This course will involve lectures, class hands-on activities, individual and group work, and instructor-, peer-, and self-assessment.
Required books:
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Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep learning (Vol. 1). Cambridge: MIT press. Available at: [link].
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Goldberg, Y. (2017). Neural network methods for natural language processing. Synthesis Lectures on Human Language Technologies, 10(1), 1-309.
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Jurafsky, D., & Martin, J. H. (2017). Speech and language processing. London:: Pearson. Available at [link].
Other recommended books:
- Bird, S., Klein, E., & Loper, E. (2009). Natural language processing with Python. O'Reilly Media, Inc. [link].
- Weekly readings will be assigned from materials available through the UBC library and online.
- See "Readings Section below"
| Date | Slides | Related Content | What is due/out |
|---|---|---|---|
| Mon Sept 3 | Labor Day – UBC closed | NA | |
| Wed Sept 5 | Course overview | NA | |
| Mon Sept 10 | Guest speaker (MAM@ECML) / Emilie Francis (SFU) | Misinformation; Linear Algebra I: [DLB CH02; MIT Course] | hw01 out (Canvas) |
| Wed Sept 12 | Guest speaker (MAM@ECML) / Michael Przystupa (UBC) | Linear Algebra II: [class slides]; [DLB CH02 ] | |
| Mon Sept 17 | Probability I | [dl_intro_slides;prob_slides; semEval2019_slides]; [DLB CH03; KA; Harvard Stats] | |
| Wed Sept 19 | Probability II | [DLB CH03]; KAI; KAII] | |
| Mon Sept 24 | Information Theory & Machine Learning Basics I | [info_theory_slides]; [ml_basics_slides]; [DLB CH05]; [KA: Calculus; 3B1B Essence of Calculus] | |
| Wed Sept 26 | Machine Learning Basics II | Same as last session | |
| Mon Oct 1 | Word meaning | [word_meaning_slides]; [JM CH06: Vector Semantics] | |
| Wed Oct 3 | Language Models | [language_models_slides]; [PyTorch_Linear_Regression_Tutorial]; [Theano_Linear_Regression_Tutorial]; [JM CH03]; [A neural probabilistic language model] | hw01 due & hw02 out |
| Mon Oct 8 | Thanksgiving Day – UBC closed | NA | |
| Wed Oct 10 | word vectors & Word2Vec | [word2vec_slides]; [Distributed representations of words and phrases and their compositionality]; [Efficient estimation of word representations in vector space]; [Linguistic regularities in continuous space word representations] [Enriching Word Vectors with Subword Information] | |
| Mon Oct 15 | Feedforward Networks | [feed_forward_nets_slides]; [DLB CH06] | |
| Wed Oct 17 | Embeddings from Language Models / Guest Speaker Mark Neuman (AI2) | [semi-supervised seq tagging with bidirectional models]; [ELMo]; [Dissecting Contextual Word Embeddings] | |
| Mon Oct 22 | Recurrent Neural Networks I | [RNN_slides]; [DLB CH10] | |
| Wed Oct 24 | Recurrent Neural Networks II | [DLB CH10] | |
| Mon Oct 29 | Applications: Text Classification; Project Discussion | [application_slides] | |
| Wed Oct 31 | Gradient-based optimization | [optimization_slides] | hw02 due |
| Mon Nov 5 | Applications | [application_slides] | hw03_a due |
| Wed Nov 7 | Seq2Seq; Neural Machine Translation | [rnn2_slides]; [Grammar as a foreign language]; [Asma: A system for Arabic Morphosyntax] | |
| Mon Nov 12 | Remembrance Day – UBC closed | NA | |
| Wed Nov 14 | AI & Society / Guest Speaker Christine Wolf (IBM); Applications: Lexical Dialectology; Backprobagation I | [Christine_slides]; [backpropagation_slides]; [Deep Models for Arabic Dialect Identification on Benchmarked Data]; [A Neural Model for User Geolocation and Lexical Dialectology] | |
| Mon Nov 19 | Backprobagation II; ConvNets I | [backpropagation_slides]; [ConvNet_slides]; [DLB CH09] | |
| Wed Nov 21 | ConvNets II; LSTMs & GRUs | [CNN_slides]; [LSTM_slides] | |
| Mon Nov 26 | Attention; Overview of (Variational) Auto-Encoders; Generative Adversarial Networks | [attention_slides] ; [Neural Machine Translation by Jointly Learning to Align and Translate]; [DLB CH20.10.4] ; [DLB CH14]; [DLB CH20.10.3]; [Semi-supervised sequence learning] | |
| Wed Nov 28 | Projects | [autoenoders_gans_slides] | |
| Mon Dec 3 | -- | Final project due (hw03_b) |
See Section 7 above. Additionally, below is list of relevant/interesting/background papers. This list will grow.
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[Joint Embedding of Words and Labels for Text Classification]
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[Learning Adversarial Networks for Semi-Supervised Text Classification via Policy Gradient]
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[Realistic Evaluation of Semi-Supervised Learning Algorithms]
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[Improving language understanding by generative pre-training]
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[Automatic Stance Detection Using End-to-End Memory Networks]
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[Neural Machine Translation by Jointly Learning to Align and Translate]
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[Deep Models for Arabic Dialect Identification on Benchmarked Data]
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[A Neural Model for User Geolocation and Lexical Dialectology]
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[Emonet: Fine-grained emotion detection with gated recurrent neural networks]
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[Neural machine translation and sequence-to-sequence models: A tutorial]
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[Adversarial training methods for semi-supervised text classification]
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[Variational Autoencoder for Semi-Supervised Text Classification]
| Assignment | Due date | Weight |
|---|---|---|
| Professionalization & Class Participation | Throughout | 15% |
| ASSIGNMENT 1: Individual assignment: Word Embeddings | Oct 3 | 15% |
| ASSIGNMENT 2: Individual assignment: Text Classification with RNNs | Oct. 24 | 20% |
| ASSIGNMENT 3_A: Group assignment: Project Proposal | Nov. 5 | 5% |
| ASSIGNMENT 3_B: Group assignment: Term Project (GROUP of 3) | Dec 3 | 45% |
In this assignment, students will train a word embeddings model (using either Word2Vec of FastText) on a dataset provided to them, and use the trained model for a number of tasks. The students will write a report using the Jupyter platform describing their work. The reprot will include both the code used and the results acquired, in a step-wise, organized fashion. The students are expected to use enabling Python libraries (e.g., NLTK, gensim, scikit-learn) and illustrate their work with visualizations automatically generated from the data. A rubric will be provided to walk students through the different steps.
Deliverables: Jupyter Notebook describing the work students carried out
For this assignment, students will build a fully-fledged text classifier using RNNs, and its variations (e.g., LSTMs, GRUs). For this, the students will be provided a labeled dataset. As such, the task is cast as supervised machine learning. The students will need to demonstrate understanding of the theoritical part of the course. For example, a detailed description of pre-processing, data splits (concepts like train, dev, and test sets and/or n-fold cross validation), model capacity (e.g., the architecture of the network used in terms of layers and number of units in each layer), activation functions, regularization, cost functions, baselines and meaningful comparisons (which baselines are chosen and why), etc.
Deliverables: A short paper (4 pages main body+ 2 pages references) + Jupyter notebook
Students will then write a 4-page report + 2 extra pages for references. A maximum of 2 extra pages can be allowed, without instructor permission, but need to be justfied in a footnote in the report. Students will also need to submit an accompanying Jupyter notebook with their code. Students will be provided an ACL LaTex template to use for writing the report, and a rubric walking them through some of the major details of how to write the report.
The purposes of this assignment include:
- Identifying and describing an interesting (i.e., challenging, novel, socially-relevant, has practical applications) NLP problem;
- Reviewing previous literature on relevant to the task, showing a clear understanding of previous scholarship;
- Proposing and developing (in the engineering sense via code) a solution to the problem using deep learning;
- Clearly describing the work, justifying decisons made, in a way that lends work to replication;
- Developing oral and written communication skills through discussions with classmates and instructor;
- Demonstrating ability to work as part of a team, including initiative taking, integrity, dependability and co-operation, and effective collaboration.
For this assignment, each student is required to work as part of a group of of 3* on a project involving a practical task. Example projects will be discussed in class.
- A group of a different size may be possible after consultation with the instructor.
Deliverables Proposal: 1-2 pages in ACL Style LaTex
- Who are the the group members?
- What is the problem you are proposing a solution for?
- What motivates your work? Why is it important or useful to undertake the chosen task?
- How does your project compare to NLP and deep learning published research?
- What are the different steps you will take to ensure success of the project? What are the smaller segments of which the bigger task is composed? And how will you conduct each small task?
- How does the work bread down and what each member of the team be contributing?
- Timeline for completing the project, including goals for each segment. Note: You may like to provide a table breaking down who is doing what and when. The LaTex files will be provided to you as part of the rubric for the final project.
Final Paper: 6-8 pages (ACL Style) The final deliverable includes:
- A detailed and clear description of your project, including the necessary sections, as appropriate. For example, you will need to include an abstract, introduction, possibly research questions, a literature review, a description of dataset (collection, splits), methods (implementation details, experimental conditions, settings, comparisons, baselines), results (in tables), analysis of results (e.g., with visulaizations, error analyses), and a conclusion describing limitations and future directions;
- All data used, whenever possible, and/or links to where the data are housed;
- Pointers to a live version of the project, if any;
- As appropriate, you should situate your work within the wider context of published works and approaches, with supporting arguments. You will have an unlimited number of pages for references, but you should plan to have at least 15 sources;
- Employment of figures, tables, and visualizations as appropriate to enhance argument and facilitate communicating your findings/results;
Attendance: The UBC calendar states: “Regular attendance is expected of students in all their classes (including lectures, laboratories, tutorials, seminars, etc.). Students who neglect their academic work and assignments may be excluded from the final examinations. Students who are unavoidably absent because of illness or disability should report to their instructors on return to classes.”
Evaluation: All assignments will be marked using the evaluative criteria given in this syllabus.
Access & Diversity: Access & Diversity works with the University to create an inclusive living and learning environment in which all students can thrive. The University accommodates students with disabilities who have registered with the Access and Diversity unit. You must register with the Disability Resource Centre to be granted special accommodations for any on-going conditions. Religious Accommodation: The University accommodates students whose religious obligations conflict with attendance, submitting assignments, or completing scheduled tests and examinations. Please let your instructor know in advance, preferably in the first week of class, if you will require any accommodation on these grounds. Students who plan to be absent for varsity athletics, family obligations, or other similar commitments, cannot assume they will be accommodated, and should discuss their commitments with the instructor before the course drop date. UBC policy on Religious Holidays.
Plagiarism Plagiarism is the most serious academic offence that a student can commit. Regardless of whether or not it was committed intentionally, plagiarism has serious academic consequences and can result in expulsion from the university. Plagiarism involves the improper use of somebody else's words or ideas in one's work.
It is your responsibility to make sure you fully understand what plagiarism is. Many students who think they understand plagiarism do in fact commit what UBC calls "reckless plagiarism." Below is an excerpt on reckless plagiarism from UBC Faculty of Arts' leaflet, Plagiarism Avoided: Taking Responsibility for Your Work.
"The bulk of plagiarism falls into this category. Reckless plagiarism is often the result of careless research, poor time management, and a lack of confidence in your own ability to think critically. Examples of reckless plagiarism include:
- Taking phrases, sentences, paragraphs, or statistical findings from a variety of sources and piecing them together into an essay (piecemeal plagiarism);
- Taking the words of another author and failing to note clearly that they are not your own. In other words, you have not put a direct quotation within quotation marks;
- Using statistical findings without acknowledging your source;
- Taking another author's idea, without your own critical analysis, and failing to acknowledge that this idea is not yours;
- Paraphrasing (i.e. rewording or rearranging words so that your work resembles, but does not copy, the original) without acknowledging your source;
- Using footnotes or material quoted in other sources as if they were the results of your own research; and
- Submitting a piece of work with inaccurate text references, sloppy footnotes, or incomplete source (bibliographic) information."
Bear in mind that this is only one example of the different forms of plagiarism. Before preparing for their written assignments, students are strongly encouraged to familiarize themselves with the following source on plagiarism: the Academic Integrity Resource Centre.
If after reading these materials you still are unsure about how to properly use sources in your work, please ask me for clarification. Students are held responsible for knowing and following all University regulations regarding academic dishonesty. If a student does not know how to properly cite a source or what constitutes proper use of a source it is the student's personal responsibility to obtain the needed information and to apply it within University guidelines and policies. If evidence of academic dishonesty is found in a course assignment, previously submitted work in this course may be reviewed for possible academic dishonesty and grades modified as appropriate. UBC policy requires that all suspected cases of academic dishonesty must be forwarded to the Dean for possible action.