Seminar "Selected Topics in Human Language Technology and Pattern Recognition"

In the Winter Term 2018 / 2019 the Lehrstuhl Informatik 6 will host a seminar entitled "Selected Topics in Human Language Technology and Pattern Recognition".

Prerequisites for participation in the seminar

Seminar format and important dates

The final presentations are scheduled as follows, as announced on L2P: Please note the following deadlines:

Note: 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 Thursday, Sept. 20, 2018.



Topics, relevant references and participants

    1. Natural Language Understanding

      1. Neural network based natural language understanding (Berger; Betreuer: Kazuki Irie (Jan Rosendahl))
        Initial References:
        • [Intent classification] S. Ravuri, and A. Stolcke, "Recurrent Neural Network and LSTM Models for Lexical Utterance Classification," in Proc. Interspeech, pages 135-139, Dresden, Germany, September 2015. https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/RNNLM_addressee.pdf
        • [Slot filling] G. Mesnil, Y. Dauphin, K. Yao, Y. Bengio, L. Deng, D. Hakkani-Tur, X. He, L. Heck, G. Tur, D. Yu, and G. Zweig, "Using Recurrent Neural Networks for Slot Filling in Spoken Language Understanding," IEEE/ACM Transactions on Audio, Speech, and Language Processing, Vol. 23, No. 3, March 2015, pages 530-539. https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/44628.pdf

    2. Sentiment Analysis from Audio

      1. Emotion detection (Lauterbach; Betreuer: Eugen Beck (Yunsu Kim))
        Initial References:
        • G. Trigeorgis et al., "Adieu features? End-to-end speech emotion recognition using a deep convolutional recurrent network," 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, 2016, pp. 5200-5204.
        • Ghosh, S., Laksana, E., Morency, L.P. and Scherer, S., 2016, September. Representation Learning for Speech Emotion Recognition. In INTERSPEECH (pp. 3603-3607).

    1. Speech Synthesis

      1. Auto-regressive models (Südholt; Betreuer: Albert Zeyer)
        Initial References:
        • Efficient Neural Audio Synthesis, https://arxiv.org/abs/1802.08435
        • PixelCNN++, https://arxiv.org/abs/1701.05517

      1. End-to-end text-to-speech (Fischer; Betreuer: Albert Zeyer)
        Initial References:
        • VoiceLoop: Voice Fitting and Synthesis via a Phonological Loop, https://arxiv.org/abs/1707.06588
        • Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions, https://arxiv.org/abs/1712.05884

    1. Text Summarization

      1. Extractive Text Summarization (Freund; Betreuer: Jan Rosendahl)
        Initial References:
        • Text Summarization Techniques: A Brief Survey. Mehdi Allahyari, Seyedamin Pouriyeh et. al. 2017. https://arxiv.org/abs/1707.02268
        • Automatic Text Summarization (book). Torres-Moreno, Juan-Manuel, 2014. http://onlinelibrary.wiley.com/book/10.1002/9781119004752 (RWTH Aachen Network)

      2. Abstractive Text Summarization (with Deep Learning) (Friedberger; Betreuer: Julian Schamper)
        Initial References:
        • Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond. Ramesh Nallapati, Bowen Zhou et. al. 2016. CoNLL. http://www.aclweb.org/anthology/K16-1028
        • Get To The Point: Summarization with Pointer-Generator Networks. Abigail See, Peter J. Liu et. al. 2017. ACL. http://aclweb.org/anthology/P17-1099

    2. Sentiment Analysis of Text

      1. Document/Sentence Level Sentiment Analysis (Makarov; Betreuer: Yunsu Kim)
        Initial References:
        • Z. Yang, D. Yang, C. Dyer, X. He, A. Smola, E. Hovy, "Hierarchical Attention Networks for Document Classification", NAACL-HLT 2016, http://www.aclweb.org/anthology/N16-1174
        • X. Wang, W. Jiang, Z. Luo, "Combination of Convolutional and Recurrent Neural Network for Sentiment Analysis of Short Texts", COLING 2016, http://www.aclweb.org/anthology/C16-1229

      2. Aspect Level Sentiment Analysis (Tran; Betreuer: Yunsu Kim)
        Initial References:
        • Y. Wang, M. Huang, L. Zhao, X. Zhu, "Attention-based LSTM for Aspect-level Sentiment Classification", EMNLP 2016, https://aclweb.org/anthology/D16-1058
        • P. Chen, Z. Sun, L. Bing, W. Yang, "Recurrent Attention Network on Memory for Aspect Sentiment Analysis", EMNLP 2017, http://aclweb.org/anthology/D17-1047


    Guidelines for the article and presentation

    The roughly 20-page article together with the slides (between 20 & 30) for the presentation should be prepared in LaTeX format. Presentations will consist of 30 to 40 minutes presentation time & 15 minutes discussion time. Document templates for both the article and the presentation slides are provided below along with links to LaTeX documentation available online. The article and the slides should be prepared in LaTeX format and submitted electronically in pdf format. Other formats will not be accepted.

                 General:
                 Specific:

    Contact

    Inquiries should be directed to the respective supervisors or to:

    Dr. Ralf Schlüter
    RWTH Aachen University
    Lehrstuhl Informatik 6
    Ahornstr. 55
    52074 Aachen

    Room 6107
    Tel: 0241 80 21630

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