3 citations · 3 across the 1 of their papers we have counts for
6 papers
What Taggers Fail to Learn, Parsers Need the Most
Mark Anderson, Carlos Gómez-Rodríguez
We present an error analysis of neural UPOS taggers to evaluate why using gold standard tags has such a large positive contribution to parsing performance while using predicted UPO…
On the Frailty of Universal POS Tags for Neural UD Parsers
Mark Anderson, Carlos Gómez-Rodríguez
We present an analysis on the effect UPOS accuracy has on parsing performance. Results suggest that leveraging UPOS tags as features for neural parsers requires a prohibitively hig…
Distilling Neural Networks for Greener and Faster Dependency Parsing
Mark Anderson, Carlos Gómez-Rodríguez
The carbon footprint of natural language processing research has been increasing in recent years due to its reliance on large and inefficient neural network implementations. Distil…
Efficient EUD Parsing
Mathieu Dehouck, Mark Anderson, Carlos Gómez-Rodríguez
We present the system submission from the FASTPARSE team for the EUD Shared Task at IWPT 2020. We engaged with the task by focusing on efficiency. For this we considered training c…
Inherent Dependency Displacement Bias of Transition-Based Algorithms
Mark Anderson, Carlos Gómez-Rodríguez
A wide variety of transition-based algorithms are currently used for dependency parsers. Empirical studies have shown that performance varies across different treebanks in such a w…
Artificially Evolved Chunks for Morphosyntactic Analysis
Mark Anderson, David Vilares, Carlos Gómez-Rodríguez
We introduce a language-agnostic evolutionary technique for automatically extracting chunks from dependency treebanks. We evaluate these chunks on a number of morphosyntactic tasks…