Deep Neural Baselines for Computational Paralinguistics

no code implementations • 5 Jul 2019 • Daniel Elsner, Stefan Langer, Fabian Ritz, Robert Müller, Steffen Illium

Detecting sleepiness from spoken language is an ambitious task, which is addressed by the Interspeech 2019 Computational Paralinguistics Challenge (ComParE).

Audio Classification BIG-bench Machine Learning +1

Soccer Team Vectors

no code implementations • 30 Jul 2019 • Robert Müller, Stefan Langer, Fabian Ritz, Christoph Roch, Steffen Illium, Claudia Linnhoff-Popien

In this work we present STEVE - Soccer TEam VEctors, a principled approach for learning real valued vectors for soccer teams where similar teams are close to each other in the resulting vector space.

BIG-bench Machine Learning

Difficulty Classification of Mountainbike Downhill Trails utilizing Deep Neural Networks

no code implementations • 5 Aug 2019 • Stefan Langer, Robert Müller, Kyrill Schmid, Claudia Linnhoff-Popien

The difficulty of mountainbike downhill trails is a subjective perception.

Classification General Classification

Content-based Recommendations for Radio Stations with Deep Learned Audio Fingerprints

no code implementations • 15 Jul 2020 • Stefan Langer, Liza Obermeier, André Ebert, Markus Friedrich, Emma Munisamy, Claudia Linnhoff-Popien

That is why finding stations playing the preferred content is a tough task for a potential listener, especially due to the overwhelming number of offered choices.

Recommendation Systems

Neural Text Classification and Stacked Heterogeneous Embeddings for Named Entity Recognition in SMM4H 2021

no code implementations • NAACL (SMM4H) 2021 • Usama Yaseen, Stefan Langer

Our text classification submissions (team:MIC-NLP) have achieved competitive performance with F1-score of $0. 46$ and $0. 90$ on ADE Classification (Task 1a) and Profession Classification (Task 7a) respectively.

named-entity-recognition Named Entity Recognition +3

Data Augmentation for Low-Resource Named Entity Recognition Using Backtranslation

1 code implementation • ICON 2021 • Usama Yaseen, Stefan Langer

The state of art natural language processing systems relies on sizable training datasets to achieve high performance.

Data Augmentation Low Resource Named Entity Recognition +3

NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation

2 code implementations • 6 Dec 2021 • Kaustubh D. Dhole, Varun Gangal, Sebastian Gehrmann, Aadesh Gupta, Zhenhao Li, Saad Mahamood, Abinaya Mahendiran, Simon Mille, Ashish Shrivastava, Samson Tan, Tongshuang Wu, Jascha Sohl-Dickstein, Jinho D. Choi, Eduard Hovy, Ondrej Dusek, Sebastian Ruder, Sajant Anand, Nagender Aneja, Rabin Banjade, Lisa Barthe, Hanna Behnke, Ian Berlot-Attwell, Connor Boyle, Caroline Brun, Marco Antonio Sobrevilla Cabezudo, Samuel Cahyawijaya, Emile Chapuis, Wanxiang Che, Mukund Choudhary, Christian Clauss, Pierre Colombo, Filip Cornell, Gautier Dagan, Mayukh Das, Tanay Dixit, Thomas Dopierre, Paul-Alexis Dray, Suchitra Dubey, Tatiana Ekeinhor, Marco Di Giovanni, Tanya Goyal, Rishabh Gupta, Louanes Hamla, Sang Han, Fabrice Harel-Canada, Antoine Honore, Ishan Jindal, Przemyslaw K. Joniak, Denis Kleyko, Venelin Kovatchev, Kalpesh Krishna, Ashutosh Kumar, Stefan Langer, Seungjae Ryan Lee, Corey James Levinson, Hualou Liang, Kaizhao Liang, Zhexiong Liu, Andrey Lukyanenko, Vukosi Marivate, Gerard de Melo, Simon Meoni, Maxime Meyer, Afnan Mir, Nafise Sadat Moosavi, Niklas Muennighoff, Timothy Sum Hon Mun, Kenton Murray, Marcin Namysl, Maria Obedkova, Priti Oli, Nivranshu Pasricha, Jan Pfister, Richard Plant, Vinay Prabhu, Vasile Pais, Libo Qin, Shahab Raji, Pawan Kumar Rajpoot, Vikas Raunak, Roy Rinberg, Nicolas Roberts, Juan Diego Rodriguez, Claude Roux, Vasconcellos P. H. S., Ananya B. Sai, Robin M. Schmidt, Thomas Scialom, Tshephisho Sefara, Saqib N. Shamsi, Xudong Shen, Haoyue Shi, Yiwen Shi, Anna Shvets, Nick Siegel, Damien Sileo, Jamie Simon, Chandan Singh, Roman Sitelew, Priyank Soni, Taylor Sorensen, William Soto, Aman Srivastava, KV Aditya Srivatsa, Tony Sun, Mukund Varma T, A Tabassum, Fiona Anting Tan, Ryan Teehan, Mo Tiwari, Marie Tolkiehn, Athena Wang, Zijian Wang, Gloria Wang, Zijie J. Wang, Fuxuan Wei, Bryan Wilie, Genta Indra Winata, Xinyi Wu, Witold Wydmański, Tianbao Xie, Usama Yaseen, Michael A. Yee, Jing Zhang, Yue Zhang

Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on.

Data Augmentation

Domain Adaptive Pretraining for Multilingual Acronym Extraction

no code implementations • 30 Jun 2022 • Usama Yaseen, Stefan Langer

This paper presents our findings from participating in the multilingual acronym extraction shared task SDU@AAAI-22.

FhGenie: A Custom, Confidentiality-preserving Chat AI for Corporate and Scientific Use

no code implementations • 29 Feb 2024 • Ingo Weber, Hendrik Linka, Daniel Mertens, Tamara Muryshkin, Heinrich Opgenoorth, Stefan Langer

Since OpenAI's release of ChatGPT, generative AI has received significant attention across various domains.