Papers by Robert Schwarzenberg
Train, Sort, Explain: Learning to Diagnose Translation Models (N19-4)
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| Challenge: | Evaluating translation models is a trade-off between effort and detail. |
| Approach: | They propose to use a neural text classifier to automatically expose systematic differences between human and machine translations to human experts. |
| Outcome: | The proposed method exposes systematic differences between human and machine translations to human experts. |
Layerwise Relevance Visualization in Convolutional Text Graph Classifiers (D19-53)
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| Challenge: | Existing explainability methods do not focus on intermediate states in hidden layers of Deep Neural Networks (DNNs). |
| Approach: | They propose a method that visits visible and hidden layers of a deep neural network and projects them onto the interpretable domain. |
| Outcome: | The proposed method yields meaningful layerwise explanations for a GCN sentence classifier. |
Thermostat: A Large Collection of NLP Model Explanations and Analysis Tools (2021.emnlp-demo)
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| Challenge: | Arras et al. (2016): explainability methods are perceived as opaque due to their complexity. |
| Approach: | They propose to use model explanations and analysis tools to facilitate research . they use a dataset that took 10k GPU hours to compile and analyse . |
| Outcome: | Thermostat allows easy access to over 200k explanations for state-of-the-art models . dataset took over 10k GPU hours (> one year) to compile; saves time . |
Abstractive Text Summarization based on Language Model Conditioning and Locality Modeling (2020.lrec-1)
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Dmitrii Aksenov, Julian Moreno-Schneider, Peter Bourgonje, Robert Schwarzenberg, Leonhard Hennig, Georg Rehm
| Challenge: | Abstractive summarization is an NLP task with many real-world applications. |
| Approach: | They propose to use a pre-trained language model to train a Transformer-based neural model . they propose a new method of BERT-windowing to allow chunk-wise processing of texts longer than the BERT window size . |
| Outcome: | The proposed model outperforms baseline models on CNN/Daily Mail dataset and shows its superiority on German dataset. |