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Found 8 papers, 1 papers with code
Comparing linear and neural models for competitive MWE identification
In this paper, we compare the use of linear versus neural classifiers in a greedy transition system for MWE identification.
Syntax-based identification of light-verb constructions
This paper analyzes results on light-verb construction identification from the PARSEME shared-task, distinguishing between simple cases that could be directly learned from training data from more complex cases that require an extra level of semantic processing.
Edition 1.2 of the PARSEME Shared Task on Semi-supervised Identification of Verbal Multiword Expressions
We present edition 1. 2 of the PARSEME shared task on identification of verbal multiword expressions (VMWEs).
Tâches auxiliaires pour l’analyse biaffine en graphes de dépendances (Auxiliary tasks to boost Biaffine Semantic Dependency Parsing)
L’analyseur biaffine de Dozat & Manning (2017), qui produit des arbres de dépendances syntaxiques, a été étendu avec succès aux graphes de dépendances syntaxico-sémantiques (Dozat & Manning, 2018).
The Self-Contained Negation Test Set
In this article, we build on Gubelmann and Handschuh (2022), which studies the modification of PLMs' predictions as a function of the polarity of inputs, in English.
Probing structural constraints of negation in Pretrained Language Models
We find that contextual representations of tokens inside the negation scope do allow for (i) a better prediction of the presence of not compared to those outside the scope and (ii) a better prediction of the right polarity of a masked polarity item licensed by not, although the magnitude of the difference varies from PLM to PLM.
Injecting Wiktionary to improve token-level contextual representations using contrastive learning
We also propose two new WiC test sets for which we show that our fine-tuning method achieves substantial improvements.
Auxiliary Tasks to Boost Biaffine Semantic Dependency Parsing
The biaffine parser of Dozat and Manning (2017) was successfully extended to semantic dependency parsing (SDP) (Dozat and Manning, 2018).
