Papers by Franck Dary

2 papers
Typological Features for Multilingual Delexicalised Dependency Parsing (N19-1)

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Challenge: Existing universal models to describe the syntax of languages are debated for decades . a new study examines the plausibility of universal grammars in dependency parsing .
Approach: They propose to use typological features to describe the syntax of languages to train a multilingual dependency parser.
Outcome: The proposed model can be trained on 40 languages with the help of typological features.
Dependency Parsing with Backtracking using Deep Reinforcement Learning (2022.tacl-1)

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Challenge: Greedy algorithms for NLP such as transition-based parsing are prone to error propagation.
Approach: They propose to allow transition-based parsing to backtrack in cases where evidence contradicts the current solution.
Outcome: The proposed behavior can be implemented on POS tagging and dependency parsing . it shows that backtracking is an effective means to fight error propagation .

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