Wesley De Neve


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Explaining Character-Aware Neural Networks for Word-Level Prediction: Do They Discover Linguistic Rules?
Fréderic Godin | Kris Demuynck | Joni Dambre | Wesley De Neve | Thomas Demeester
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Character-level features are currently used in different neural network-based natural language processing algorithms. However, little is known about the character-level patterns those models learn. Moreover, models are often compared only quantitatively while a qualitative analysis is missing. In this paper, we investigate which character-level patterns neural networks learn and if those patterns coincide with manually-defined word segmentations and annotations. To that end, we extend the contextual decomposition technique (Murdoch et al. 2018) to convolutional neural networks which allows us to compare convolutional neural networks and bidirectional long short-term memory networks. We evaluate and compare these models for the task of morphological tagging on three morphologically different languages and show that these models implicitly discover understandable linguistic rules.


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Improving Language Modeling using Densely Connected Recurrent Neural Networks
Fréderic Godin | Joni Dambre | Wesley De Neve
Proceedings of the 2nd Workshop on Representation Learning for NLP

In this paper, we introduce the novel concept of densely connected layers into recurrent neural networks. We evaluate our proposed architecture on the Penn Treebank language modeling task. We show that we can obtain similar perplexity scores with six times fewer parameters compared to a standard stacked 2-layer LSTM model trained with dropout (Zaremba et al., 2014). In contrast with the current usage of skip connections, we show that densely connecting only a few stacked layers with skip connections already yields significant perplexity reductions.


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Multimedia Lab @ ACL WNUT NER Shared Task: Named Entity Recognition for Twitter Microposts using Distributed Word Representations
Fréderic Godin | Baptist Vandersmissen | Wesley De Neve | Rik Van de Walle
Proceedings of the Workshop on Noisy User-generated Text