Marek Grześ

Also published as: Marek Grzes


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SESAM at SemEval-2020 Task 8: Investigating the Relationship between Image and Text in Sentiment Analysis of Memes
Lisa Bonheme | Marek Grzes
Proceedings of the Fourteenth Workshop on Semantic Evaluation

This paper presents our submission to task 8 (memotion analysis) of the SemEval 2020 competition. We explain the algorithms that were used to learn our models along with the process of tuning the algorithms and selecting the best model. Since meme analysis is a challenging task with two distinct modalities, we studied the impact of different multimodal representation strategies. The results of several approaches to dealing with multimodal data are therefore discussed in the paper. We found that alignment-based strategies did not perform well on memes. Our quantitative results also showed that images and text were uncorrelated. Fusion-based strategies did not show significant improvements and using one modality only (text or image) tends to lead to better results when applied with the predictive models that we used in our research.


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Relating RNN Layers with the Spectral WFA Ranks in Sequence Modelling
Farhana Ferdousi Liza | Marek Grzes
Proceedings of the Workshop on Deep Learning and Formal Languages: Building Bridges

We analyse Recurrent Neural Networks (RNNs) to understand the significance of multiple LSTM layers. We argue that the Weighted Finite-state Automata (WFA) trained using a spectral learning algorithm are helpful to analyse RNNs. Our results suggest that multiple LSTM layers in RNNs help learning distributed hidden states, but have a smaller impact on the ability to learn long-term dependencies. The analysis is based on the empirical results, however relevant theory (whenever possible) was discussed to justify and support our conclusions.


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An Improved Crowdsourcing Based Evaluation Technique for Word Embedding Methods
Farhana Ferdousi Liza | Marek Grześ
Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for NLP