Papers by Mathieu Dehouck
Phylogenic Multi-Lingual Dependency Parsing (N19-1)
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| Challenge: | phylogenetic learning is beneficial for low resourced languages and well furnished languages families. |
| Approach: | They propose to use the phylogenetic tree to guide the learning of multi-lingual dependency parsers . they use a phylogy tree to train models that leverage languages structural similarities . |
| Outcome: | The proposed model outperforms independently learned models on zero-shot parsing of unseen languages. |
Data Augmentation via Subtree Swapping for Dependency Parsing of Low-Resource Languages (2020.coling-main)
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| Challenge: | Lack of annotated training data is a big issue for building reliable NLP systems for most of the world’s languages. |
| Approach: | They propose a method to swap subtrees between annotated sentences while enforcing strong constraints on those trees to ensure maximum grammaticality of the new sentences. |
| Outcome: | The proposed method outperforms previous methods using the same inputs and using low-resource languages. |
A Framework for Understanding the Role of Morphology in Universal Dependency Parsing (D18-1)
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| Challenge: | a measure of morphological complexity is used to characterize syntactic information in word embeddings. |
| Approach: | They propose a measure of morphological complexity in terms of governor-dependent preferential attachment that explains parsing performance. |
| Outcome: | The proposed framework improves parsing performance on morphologically rich languages using morphology as a syntactic marker. |