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Found 17 papers, 6 papers with code
gaBERT — an Irish Language Model
We compare our gaBERT model to multilingual BERT and the monolingual Irish WikiBERT, and we show that gaBERT provides better representations for a downstream parsing task.
Naive Bayes versus BERT: Jupyter notebook assignments for an introductory NLP course
We describe two Jupyter notebooks that form the basis of two assignments in an introductory Natural Language Processing (NLP) module taught to final year undergraduate students at Dublin City University.
Revisiting Tri-training of Dependency Parsers
We compare two orthogonal semi-supervised learning techniques, namely tri-training and pretrained word embeddings, in the task of dependency parsing.
gaBERT -- an Irish Language Model
We compare our gaBERT model to multilingual BERT and the monolingual Irish WikiBERT, and we show that gaBERT provides better representations for a downstream parsing task.
The DCU-EPFL Enhanced Dependency Parser at the IWPT 2021 Shared Task
The task involves parsing Enhanced UD graphs, which are an extension of the basic dependency trees designed to be more facilitative towards representing semantic structure.
The ADAPT Enhanced Dependency Parser at the IWPT 2020 Shared Task
Unfortunately, we did not ensure a connected graph as part of our pipeline approach and our competition submission relied on a last-minute fix to pass the validation script which harmed our official evaluation scores significantly.
Treebank Embedding Vectors for Out-of-domain Dependency Parsing
A recent advance in monolingual dependency parsing is the idea of a treebank embedding vector, which allows all treebanks for a particular language to be used as training data while at the same time allowing the model to prefer training data from one treebank over others and to select the preferred treebank at test time.
Cross-lingual Parsing with Polyglot Training and Multi-treebank Learning: A Faroese Case Study
Finally, we apply multi-treebank modelling to the projected treebanks, in addition to or alternatively to polyglot modelling on the source side.
APE through Neural and Statistical MT with Augmented Data. ADAPT/DCU Submission to the WMT 2019 APE Shared Task
Automatic post-editing (APE) can be reduced to a machine translation (MT) task, where the source is the output of a specific MT system and the target is its post-edited variant.
