Papers by Andreas van Cranenburgh
What’s so special about BERT’s layers? A closer look at the NLP pipeline in monolingual and multilingual models (2020.findings-emnlp)
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| Challenge: | In addition, information on part-of-speech tagging is spread over different parts of the network and the pipeline might not be as neat as it seems. |
| Approach: | They propose to probe Dutch BERT-based model and multilingual BERT model for Dutch NLP tasks to see if this holds true for other languages. |
| Outcome: | The proposed model is based on a Dutch model and a multilingual model for Dutch NLP tasks. |
Active DOP: A constituency treebank annotation tool with online learning (C18-2)
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| Challenge: | a new language-independent treebank annotation tool supports rich annotations with discontinuous constituents and function tags. |
| Approach: | They propose a language-independent treebank annotation tool supporting rich annotations with discontinuous constituents and function tags. |
| Outcome: | The proposed tool supports rich annotations with discontinuous constituents and function tags. |
German and French Neural Supertagging Experiments for LTAG Parsing (P18-3)
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| Challenge: | Lexicalized Tree Adjoining Grammars are a linguistically motivated grammar formalism that allows parsers to express linguistic generalizations that are not captured by statistical parsing. |
| Approach: | They propose a supertagging approach combined with deep learning to extract LTAG supertags from the French Treebank and propose n-best supertailing for German and French. |
| Outcome: | The proposed supertagging approach is able to extract LTAG supertags from the French Treebank and n-best supertracking for German and German. |
Embarrassingly Simple Unsupervised Aspect Extraction (2020.acl-main)
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| Challenge: | Existing systems for aspect extraction are supervised, but are unlikely to transfer well between domains. |
| Approach: | They propose a novel approach that uses an RBF kernel to generate a single-head attention mechanism for aspect extraction from text. |
| Outcome: | The proposed method is based on an RBF kernel and can be applied to new domains and languages. |