Document Open Access Logo

Introducing the NLU Showroom: A NLU Demonstrator for the German Language

Authors Dennis Wegener, Sven Giesselbach, Niclas Doll, Heike Horstmann

PDF
Thumbnail PDF

File

OASIcs.LDK.2021.28.pdf
  • Filesize: 0.74 MB
  • 9 pages

Document Identifiers

Author Details

Dennis Wegener
  • Fraunhofer Institute for Intelligent Analysis and Information Systems, Sankt Augustin, Germany
Sven Giesselbach
  • Fraunhofer Institute for Intelligent Analysis and Information Systems, Sankt Augustin, Germany
Niclas Doll
  • Fraunhofer Institute for Intelligent Analysis and Information Systems, Sankt Augustin, Germany
Heike Horstmann
  • Fraunhofer Institute for Intelligent Analysis and Information Systems, Sankt Augustin, Germany

Funding

This research has been funded by the Federal Ministry of Education and Research of Germany as part of the competence center for machine learning ML2R (01IS18038B).

Cite As Get BibTex

Dennis Wegener, Sven Giesselbach, Niclas Doll, and Heike Horstmann. Introducing the NLU Showroom: A NLU Demonstrator for the German Language. In 3rd Conference on Language, Data and Knowledge (LDK 2021). Open Access Series in Informatics (OASIcs), Volume 93, pp. 28:1-28:9, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021) https://doi.org/10.4230/OASIcs.LDK.2021.28

Abstract

We present the NLU Showroom, a platform for interactively demonstrating the functionality of natural language understanding models with easy to use visual interfaces. The NLU Showroom focuses primarily on the German language, as not many German NLU resources exist. However, it also serves corresponding English models to reach a broader audience. With the NLU Showroom we demonstrate and compare the capabilities and limitations of a variety of NLP/NLU models. The four initial demonstrators include a) a comparison on how different word representations capture semantic similarity b) a comparison on how different sentence representations interpret sentence similarity c) a showcase on analyzing reviews with NLU d) a showcase on finding links between entities. The NLU Showroom is build on state-of-the-art architectures for model serving and data processing. It targets a broad audience, from newbies to researchers but puts a focus on putting the presented models in the context of industrial applications.

Subject Classification

ACM Subject Classification
  • Applied computing → Document management and text processing
Keywords
  • Natural Language Understanding
  • Natural Language Processing
  • NLU
  • NLP
  • Showroom
  • Demonstrator
  • Demos
  • Text Similarity
  • Opinion Mining
  • Relation Extraction

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads
    0
    Metadata Views

References

  1. AllenNLP demo. https://demo.allennlp.org. Accessed: 2021-02-02.
  2. Docker. https://www.docker.com/. Accessed: 2021-02-02.
  3. IBM Watson natural language understanding - text analysis. https://www.ibm.com/demos/live/natural-language-understanding/self-service/home. Accessed: 2021-02-02.
  4. NLTK text processing demo. http://text-processing.com/demo/. Accessed: 2021-02-02.
  5. Stanza online demo. http://stanza.run/. Accessed: 2021-02-02.
  6. TorchServe. https://github.com/pytorch/serve. Accessed: 2021-02-02.
  7. Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dandelion Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. TensorFlow: Large-scale machine learning on heterogeneous systems, 2015. Software available from tensorflow.org. URL: https://www.tensorflow.org/.
  8. Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. Enriching word vectors with subword information. CoRR, abs/1607.04606, 2016. URL: http://arxiv.org/abs/1607.04606.
  9. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171-4186, Minneapolis, Minnesota, 2019. Association for Computational Linguistics. URL: https://doi.org/10.18653/v1/N19-1423.
  10. Matt Gardner, Joel Grus, Mark Neumann, Oyvind Tafjord, Pradeep Dasigi, Nelson F. Liu, Matthew Peters, Michael Schmitz, and Luke S. Zettlemoyer. AllenNLP: A deep semantic natural language processing platform. arXiv, 2017. URL: http://arxiv.org/abs/1803.07640.
  11. Iris Hendrickx, Su Nam Kim, Zornitsa Kozareva, Preslav Nakov, Diarmuid Ó Séaghdha, Sebastian Padó, Marco Pennacchiotti, Lorenza Romano, and Stan Szpakowicz. SemEval-2010 task 8: Multi-way classification of semantic relations between pairs of nominals. In Proceedings of the 5th International Workshop on Semantic Evaluation, pages 33-38, Uppsala, Sweden, 2010. Association for Computational Linguistics. URL: https://www.aclweb.org/anthology/S10-1006.
  12. Matt Kusner, Y. Sun, N.I. Kolkin, and Kilian Weinberger. From word embeddings to document distances. Proceedings of the 32nd International Conference on Machine Learning (ICML 2015), pages 957-966, January 2015. Google Scholar
  13. Quoc V. Le and Tomas Mikolov. Distributed representations of sentences and documents. CoRR, abs/1405.4053, 2014. URL: http://arxiv.org/abs/1405.4053.
  14. Omer Levy and Yoav Goldberg. Neural word embedding as implicit matrix factorization. In Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2, NIPS'14, page 2177–2185, Cambridge, MA, USA, 2014. MIT Press. Google Scholar
  15. Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean. Distributed representations of words and phrases and their compositionality. CoRR, abs/1310.4546, 2013. URL: http://arxiv.org/abs/1310.4546.
  16. Christopher Olston, Noah Fiedel, Kiril Gorovoy, Jeremiah Harmsen, Li Lao, Fangwei Li, Vinu Rajashekhar, Sukriti Ramesh, and Jordan Soyke. Tensorflow-serving: Flexible, high-performance ML serving. CoRR, abs/1712.06139, 2017. URL: http://arxiv.org/abs/1712.06139.
  17. Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. PyTorch: An imperative style, high-performance deep learning library. In H. Wallach, H. Larochelle, A. Beygelzimer, F. dquotesingle Alché-Buc, E. Fox, and R. Garnett, editors, Advances in Neural Information Processing Systems 32, pages 8024-8035. Curran Associates, Inc., 2019. URL: http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf.
  18. Jeffrey Pennington, Richard Socher, and Christopher D. Manning. Glove: Global vectors for word representation. In Empirical Methods in Natural Language Processing (EMNLP), pages 1532-1543, 2014. URL: http://www.aclweb.org/anthology/D14-1162.
  19. Maria Pontiki, Dimitris Galanis, John Pavlopoulos, Harris Papageorgiou, Ion Androutsopoulos, and Suresh Manandhar. SemEval-2014 task 4: Aspect based sentiment analysis. In Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pages 27-35, Dublin, Ireland, 2014. Association for Computational Linguistics. URL: https://doi.org/10.3115/v1/S14-2004.
  20. Peng Qi, Timothy Dozat, Yuhao Zhang, and Christopher D. Manning. Universal dependency parsing from scratch. In Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, pages 160-170, Brussels, Belgium, October 2018. Association for Computational Linguistics. URL: https://nlp.stanford.edu/pubs/qi2018universal.pdf.
  21. Peng Qi, Yuhao Zhang, Yuhui Zhang, Jason Bolton, and Christopher D. Manning. Stanza: A Python natural language processing toolkit for many human languages. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, 2020. URL: https://nlp.stanford.edu/pubs/qi2020stanza.pdf.
  22. Georg Rehm, Maria Berger, Ela Elsholz, Stefanie Hegele, Florian Kintzel, Katrin Marheinecke, Stelios Piperidis, Miltos Deligiannis, Dimitris Galanis, Katerina Gkirtzou, Penny Labropoulou, Kalina Bontcheva, David Jones, Ian Roberts, Jan Hajič, Jana Hamrlová, Lukáš Kačena, Khalid Choukri, Victoria Arranz, Andrejs Vasiļjevs, Orians Anvari, Andis Lagzdiņš, Jūlija Meļņika, Gerhard Backfried, Erinç Dikici, Miroslav Janosik, Katja Prinz, Christoph Prinz, Severin Stampler, Dorothea Thomas-Aniola, José Manuel Gómez-Pérez, Andres Garcia Silva, Christian Berrío, Ulrich Germann, Steve Renals, and Ondrej Klejch. European language grid: An overview. In Proceedings of the 12th Language Resources and Evaluation Conference, pages 3366-3380, Marseille, France, 2020. European Language Resources Association. URL: https://www.aclweb.org/anthology/2020.lrec-1.413.
  23. Martin Schiersch, Veselina Mironova, Maximilian Schmitt, Philippe Thomas, Aleksandra Gabryszak, and Leonhard Hennig. A german corpus for fine-grained named entity recognition and relation extraction of traffic and industry events. In Proceedings of the 11th International Conference on Language Resources and Evaluation. International Conference on Language Resources and Evaluation (LREC-18), 11th, May 7-12, Miyazaki, Japan. European Language Resources Association, 2018. Google Scholar
  24. Livio Baldini Soares, Nicholas FitzGerald, Jeffrey Ling, and Tom Kwiatkowski. Matching the blanks: Distributional similarity for relation learning. CoRR, abs/1906.03158, 2019. URL: http://arxiv.org/abs/1906.03158.
  25. Chi Sun, Luyao Huang, and Xipeng Qiu. Utilizing BERT for aspect-based sentiment analysis via constructing auxiliary sentence. CoRR, abs/1903.09588, 2019. URL: http://arxiv.org/abs/1903.09588.
  26. Wenya Wang, Sinno Jialin Pan, Daniel Dahlmeier, and Xiaokui Xiao. Recursive neural conditional random fields for aspect-based sentiment analysis. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 616-626, Austin, Texas, 2016. Association for Computational Linguistics. URL: https://doi.org/10.18653/v1/D16-1059.
  27. Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, and Jamie Brew. Huggingface’s transformers: State-of-the-art natural language processing. CoRR, abs/1910.03771, 2019. URL: http://arxiv.org/abs/1910.03771.
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail
© 2023-2024 Schloss Dagstuhl – LZI GmbH About DROPS Imprint Privacy Contact