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20192022
most citedWhat Taggers Fail to Learn, Parsers Need the Most

3 citations · 3 across the 7 of their papers we have counts for

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Showing 2021 · cs.CLShow all

5 papers · 2 filters

cs.CL2021

Splitting EUD graphs into trees: A quick and clatty approach

Mark Anderson, Carlos Gómez-Rodríguez

We present the system submission from the FASTPARSE team for the EUD Shared Task at IWPT 2021. We engaged in the task last year by focusing on efficiency. This year we have focused…

cs.CL2021

A Modest Pareto Optimisation Analysis of Dependency Parsers in 2021

Mark Anderson, Carlos Gómez Rodríguez

We evaluate three leading dependency parser systems from different paradigms on a small yet diverse subset of languages in terms of their accuracy-efficiency Pareto front. As we ar…

cs.CL2021

A Falta de Pan, Buenas Son Tortas: The Efficacy of Predicted UPOS Tags for Low Resource UD Parsing

Mark Anderson, Mathieu Dehouck, Carlos Gómez Rodríguez

We evaluate the efficacy of predicted UPOS tags as input features for dependency parsers in lower resource settings to evaluate how treebank size affects the impact tagging accurac…

cs.CL2021

Replicating and Extending "Because Their Treebanks Leak": Graph Isomorphism, Covariants, and Parser Performance

Mark Anderson, Anders Søgaard, Carlos Gómez Rodríguez

Søgaard (2020) obtained results suggesting the fraction of trees occurring in the test data isomorphic to trees in the training set accounts for a non-trivial variation in parser p…

cs.CL2021★ 3 cited

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…