Simon Tannert


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FlowchartQA: The First Large-Scale Benchmark for Reasoning over Flowcharts
Simon Tannert | Marcelo G. Feighelstein | Jasmina Bogojeska | Joseph Shtok | Assaf Arbelle | Peter W. J. Staar | Anika Schumann | Jonas Kuhn | Leonid Karlinsky
Proceedings of the 1st Workshop on Linguistic Insights from and for Multimodal Language Processing


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IMSurReal Too: IMS in the Surface Realization Shared Task 2020
Xiang Yu | Simon Tannert | Ngoc Thang Vu | Jonas Kuhn
Proceedings of the Third Workshop on Multilingual Surface Realisation

We introduce the IMS contribution to the Surface Realization Shared Task 2020. The new system achieves substantial improvement over the state-of-the-art system from last year, mainly due to a better token representation and a better linearizer, as well as a simple ensembling approach. We also experiment with data augmentation, which brings some additional performance gain. The system is available at

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Fast and Accurate Non-Projective Dependency Tree Linearization
Xiang Yu | Simon Tannert | Ngoc Thang Vu | Jonas Kuhn
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

We propose a graph-based method to tackle the dependency tree linearization task. We formulate the task as a Traveling Salesman Problem (TSP), and use a biaffine attention model to calculate the edge costs. We facilitate the decoding by solving the TSP for each subtree and combining the solution into a projective tree. We then design a transition system as post-processing, inspired by non-projective transition-based parsing, to obtain non-projective sentences. Our proposed method outperforms the state-of-the-art linearizer while being 10 times faster in training and decoding.

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Interpreting Attention Models with Human Visual Attention in Machine Reading Comprehension
Ekta Sood | Simon Tannert | Diego Frassinelli | Andreas Bulling | Ngoc Thang Vu
Proceedings of the 24th Conference on Computational Natural Language Learning

While neural networks with attention mechanisms have achieved superior performance on many natural language processing tasks, it remains unclear to which extent learned attention resembles human visual attention. In this paper, we propose a new method that leverages eye-tracking data to investigate the relationship between human visual attention and neural attention in machine reading comprehension. To this end, we introduce a novel 23 participant eye tracking dataset - MQA-RC, in which participants read movie plots and answered pre-defined questions. We compare state of the art networks based on long short-term memory (LSTM), convolutional neural models (CNN) and XLNet Transformer architectures. We find that higher similarity to human attention and performance significantly correlates to the LSTM and CNN models. However, we show this relationship does not hold true for the XLNet models – despite the fact that the XLNet performs best on this challenging task. Our results suggest that different architectures seem to learn rather different neural attention strategies and similarity of neural to human attention does not guarantee best performance.


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Creating a gold standard corpus for terminological annotation from online forum data
Anna Hätty | Simon Tannert | Ulrich Heid
Proceedings of Language, Ontology, Terminology and Knowledge Structures Workshop (LOTKS 2017)