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Found 10 papers, 1 papers with code
Biaffine Dependency and Semantic Graph Parsing for EnhancedUniversal Dependencies
This paper presents the system used in our submission to the IWPT 2021 Shared Task.
Dialogue Act and Slot Recognition in Italian Complex Dialogues
Since the advent of Transformer-based, pretrained language models (LM) such as BERT, Natural Language Understanding (NLU) components in the form of Dialogue Act Recognition (DAR) and Slot Recognition (SR) for dialogue systems have become both more accurate and easier to create for specific application domains.
MATILDA - Multi-AnnoTator multi-language InteractiveLight-weight Dialogue Annotator
Dialogue Systems are becoming ubiquitous in various forms and shapes - virtual assistants(Siri, Alexa, etc.
Linear Neural Parsing and Hybrid Enhancement for Enhanced Universal Dependencies
To accomplish the shared task on dependency parsing we explore the use of a linear transition-based neural dependency parser as well as a combination of three of them by means of a linear tree combination algorithm.
Enhancing Universal Dependency Treebanks: A Case Study
We evaluate two cross-lingual techniques for adding enhanced dependencies to existing treebanks in Universal Dependencies.
Assessing the Impact of Incremental Error Detection and Correction. A Case Study on the Italian Universal Dependency Treebank
For both evaluation datasets, the performance of parsers increases, in terms of the standard LAS and UAS measures and of a more focused measure taking into account only relations involved in error patterns, and at the level of individual dependencies.
CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
The Conference on Computational Natural Language Learning (CoNLL) features a shared task, in which participants train and test their learning systems on the same data sets.
Adapting the TANL tool suite to Universal Dependencies
TANL is a suite of tools for text analytics based on the software architecture paradigm of data driven pipelines.
Less is More? Towards a Reduced Inventory of Categories for Training a Parser for the Italian Stanford Dependencies
This is done by comparing the performance of a statistical parser (DeSR) trained on a simpler resource (the augmented version of the Merged Italian Dependency Treebank or MIDT+) and whose output was automatically converted to SD, with the results of the parser directly trained on ISDT.
