Mathieu Lacroix


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Representation Learning and Dynamic Programming for Arc-Hybrid Parsing
Joseph Le Roux | Antoine Rozenknop | Mathieu Lacroix
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)

We present a new method for transition-based parsing where a solution is a pair made of a dependency tree and a derivation graph describing the construction of the former. From this representation we are able to derive an efficient parsing algorithm and design a neural network that learns vertex representations and arc scores. Experimentally, although we only train via local classifiers, our approach improves over previous arc-hybrid systems and reach state-of-the-art parsing accuracy.


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Efficient Discontinuous Phrase-Structure Parsing via the Generalized Maximum Spanning Arborescence
Caio Corro | Joseph Le Roux | Mathieu Lacroix
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

We present a new method for the joint task of tagging and non-projective dependency parsing. We demonstrate its usefulness with an application to discontinuous phrase-structure parsing where decoding lexicalized spines and syntactic derivations is performed jointly. The main contributions of this paper are (1) a reduction from joint tagging and non-projective dependency parsing to the Generalized Maximum Spanning Arborescence problem, and (2) a novel decoding algorithm for this problem through Lagrangian relaxation. We evaluate this model and obtain state-of-the-art results despite strong independence assumptions.