Seminar "Selected Topics in Human Language Technology and Machine Learning"

Registration for the seminar

Registration for the seminar is only possible online via the central registration page.

Prerequisites for Participation in the Seminar

• Einführung in das wissenschaftliche Arbeiten (Proseminar)
• Attendance of the lectures Statistical Classification and Machine Learning, Automatic Speech Recognition, and/or Statistical Methods in Natural Language Processing, or evidence of equivalent knowledge is highly recommended.
• For successful participants of the above lectures, seminar participation is guaranteed.
  

General Goals of the Seminar

• Performance of a literature review based on the initial references provided to attain on overview of the assigned seminar topic
• Reading, comprehending and critically analyzing the assigned and found articles.
• Covering relevant publications within the scope of the assigned topic and the seminar format.
• Describing the topic on this basis in a written report.
• Preparing slides and presenting the topic in an oral presentation.
• NewsKeeping the ethical guidelines for the authoring of academic work, which especially includes that all work used to prepare the seminar report and presentation is correctly cited.

Seminar Format and Important Dates

• Proposals: initial proposals will be accepted up until 2022.04.01 by email to the seminar topic's supervisor. At this time, participants must arrange an appointment with the relevant supervisor. Revised proposals will be accepted up until 2022.04.15.
• Article: PDF must be submitted until 2022.06.01 by email to the seminar topic's supervisor.
  
• Presentation slides: PDF must be submitted by 2022.06.15 by email to the seminar topic's supervisor.
  
• Trial presentations: finished by 2022.07.01.
• Seminar presentations: Tentative: July 11th - 14th, July 18th - 22nd.
• Final (possibly corrected) articles and presentation slides: PDF must be submitted 4 weeks after the presentation date at the latest by email to the seminar topic's supervisor.
• Compulsory attendance: in order to pass, participants must attend all presentation sessions.
• Ethical Guidelines:The Computer Science Department of RWTH Aachen University has adopted ethical guidelines for the authoring of academic work, such as seminar reports. Each student has to comply with these guidelines. In this regard, you, as a seminar attendant, have to sign a declaration of compliance, in which you assert that your work complies with the guidelines, that all references used are properly cited, and that the report was done autonomously by yourself. We ask you do download the guidelines and submit the declaration together with your seminar report and talk to your supervisor. You also find a German version of the declaration you may use as well.

Note: failure to comply with the ethical guidelines, failure to meet deadlines, absence without permission from compulsory sessions (presentations and preliminary meeting as announced by email to each participating student), or dropping out of the seminar after more than 3 weeks after the preliminary meeting/topic distribution results in the grade 5.0/not appeared.

The deadline for de-registration from the seminar is 2022.03.24, i.e. within three weeks after the distribution of the topics. After this deadline, seminar participation is confirmed and will be graded.

Topics, Initial References Defining the Topics, Participants, and Supervisors

• Automatic Speech Recognition;
• Machine Translation;
• Natural Language Understanding;
• Machine Learning.

1. State-of-the-Art Hybrid HMM based ASR (Student: XXX, Supervisor: Raissi, Tina)
   Initial References:
   
   • Y. Wang et al.: "Transformer-Based Acoustic Modeling for Hybrid Speech Recognition"
   • F. Zhang et al.: "Faster, Simpler and More Accurate Hybrid ASR Systems Using Wordpieces"


2. Non-Autoregressive ASR (Student: Michels, Supervisor: Raissi, Tina)
   Initial References:
   
   • Yosuke Higuchi et al.: "Mask CTC: Non-Autoregressive End-to-End ASR with CTC and Mask Predict"
   • Nanxin Chen et al.: "Listen and Fill in the Missing Letters: Non-Autoregressive Transformer for Speech Recognition"
   • Zhengkun Tian et al.: "Spike-Triggered Non-Autoregressive Transformer for End-to-End Speech Recognition"


3. Comparison of Encoder Model Topologies for ASR (Student: XXX, Supervisor: Lüscher, Christoph)
   Initial References:
   
   • A. Gulati et al.: "Conformer: Convolution-augmented Transformer for Speech Recognition"
   • Y. Wang et al.: "Transformer-based Acoustic Modeling for Hybrid Speech Recognition"


4. Multi-Speaker Voice Conversion (Student: Vogelbacher, Supervisor: Rossenbach, Nick)
   Initial References:
   
   • Hirokazu Kameoka et al.: "STARGAN-VC: NON-PARALLEL MANY-TO-MANY VOICE CONVERSION WITH STAR GENERATIVE ADVERSARIAL NETWORKS"
   • Manh Luong et al.: "Many-to-Many Voice Conversion based Feature Disentanglement using Variational Autoencoder"


5. Named Entity Recognition (Student: Nguyenová, Supervisor: Thulke, David)
   Initial References:
   
   • G. Lample et al.: "Neural Architectures for Named Entity Recognition"
   • A. Akbik et al.: "Contextual String Embeddings for Sequence Labeling"


6. Transducer Modeling for ASR (Student: Smolakovs, Supervisor: Zhou, Wei)
   Initial References:
   
   • R. Zhao et al.: "On Addressing Practical Challenges for RNN-Transducer"
   • J. Guo et al.: "Efficient minimum word error rate training of RNN-Transducer for end-to-end speech recognition"


7. Attention Models for ASR (Student: Avcu, Supervisor: Yang, Zijian)
   Initial References:
   
   • William Chan et al.: "Listen, Attend and Spell"
   • Ashish Vaswani et al.: "Attention Is All You Need"


8. Comparison of Model Topologies for Language Modeling (Student: Wan, Supervisor: Gao, Yingbo)
   Initial References:
   
   • Yoshua Bengio et al.: "A Neural Probabilistic Language Model"
   • Stephen Merity et al.: "Regularizing and Optimizing LSTM Language Models"


9. End-to-End Machine Translation (Student: Deckert, Supervisor: Herold, Christian)
   Initial References:
   
   • Ashish Vaswani et al.: "Attention is All you Need"


10. Unsupervised Training of Speech Representations (Student: Mhamdi, Supervisor: Lüscher, Christoph)
    Initial References:
    
    • Baevski, A. et al.: "wav2vec 2.0: A framework for self-supervised learning of speech representations"
    • Liu, A. et al.: "Mockingjay: Unsupervised Speech Representation Learning with Deep Bidirectional Transformer Encoders"

Article and Presentation Format

• Online LaTeX-Documentation:

• LaTeX: "A document preparation system"
• LaTeX help 1.1
• The Not So Short Introduction to LaTeX
  
• LaTeX 2e for authors

• Document Templates:

• Article Template (51kB),  contains the template and all necessary files in tar format (or here 10kB in zip format).
• New Presentation Slide Template, a zip file containing the template and all necessary graphics as well as the institute’s style template. Note: We deactivated the RWTH and i6 logos in this version of the template since the seminar content is produced by students outside of i6.

Detailed Guidelines:

• Take care to stay within your own topic. To this end participants should be aware of the other topics in the seminar. If applicable, cross-reference other articles and presentations.
• Important: As part of the introduction, a slide should outline the most important literature used for the presentation. In addition, the presentation should clearly indicate which literature the particular elements of the presentation refer to.
  
• Take notice of references to other topics in the seminar and discuss topics with one another!
  
• Participants are expected to seek out additional literature on their topic. Assistance with the literature search is available at the faculty’s library. Access to literature is naturally also available at the Lehrstuhl Informatik 6 library.
• Notation/Mathematical Formulas: consistent, correct notation is essential. When necessary, differing notation from various literature sources is to be modified or standardized in order to be clear and consistent. The lectures held by the Lehrstuhl Informatik 6 should provide a guide as to what appropriate notation should look like.
  
• Tables must have titles (appearing above the table).
• Figures must have captions (appearing below the figure).
  
• The use of English is recommended and mandatory for the presentation slides. Nevertheless, the article and oral presentation may be done in German.
  
• In the case that no adequate translation of an English technical term is available, the term should be used unchanged.
• Completeness: acknowledge all literature and sources, thus following the ethical guidelines for the authoring of academic work.
  
• Referencing must conform to the standard described in the article template.
• Examples should be used to illustrate points.
• Examples should be as complex as necessary but as simple as possible.
• Slides should be used as presentation aids and not to replace the role of the presenter; specifically, slides should:
• illustrate important points and relationships;
• remind the audience (and the presenter) of important aspects and considerations;
  
• give the audience an overview of the presentation.
  
• Slides should not contain chunks of text or complicated sentences; rather they should consist of succinct statements and use consistent terminology.
• Use illustrations where appropriate - a picture says a thousand words!
• Abbreviations should be defined at the first usage in the manner demonstrated in the following example: "[...] at the Rheinisch-Westfälischen Technischen Hochschule (RWTH) there are [...]".
  
• Usage of fonts, typefaces and colors in presentation slides must be consistent and appropriate. Such means should serve to clarify points or relationships, not be applied needlessly or at random.
  
• Care should be taken when selecting font sizes for presentation slides (also within diagrams) to ensure legibility on a projector even for those seated far from the screen.

Some Tips:

• The draft of the article and the trial presentation slides to be sent before the corresponding deadlines must be complete (in particular, they must contain a required number of pages). In principle, supervisors will not give any iterative feedback if an updated version is submitted after the deadlines. Do not miss the opportunities!

• Define the task clearly upfront (what is the problem? what are the input/output of the system?).
• Give a short overview of the existing works on the topic.
• Provide detailed descriptions of the state-of-the-art approach(es) with mathematical definitions using correct notations.
• Include meaningful experimental results (extracted from the papers) with clear definitions of the datasets and the evaluation metrics.

• Why is this paper relevant for my topic: a historical piece of work? the state-of-the-art method? Does the paper help me understanding the topic better?
• Do I really understand the paper? Can I describe how the method works without doubt? Can I explain the nature/dimension of all quantities in equations? Is this paper self-contained? or should I further read cited papers to get more background?
• Is the paper content correct and consistent with other publications on the topic? If not: try to resolve discrepancies and broach such issues when discussing the paper in your article and presentation.
• Are the experiments meaningful and convincing? Can I clearly describe the experiment and the evaluation metric?
• How does this paper relate to other papers I have read? Is the same dataset used for evaluation?

Contact

Yingbo Gao
RWTH Aachen University
Lehrstuhl Informatik 6
Ahornstr. 55
52074 Aachen

Room 6125
Tel: 0241 80 21611

E-Mail: ygao@cs.rwth-aachen.de

Last modified: 2022-04-28 10:51:23 Disclaimer.