Semantic Role Lableing with BERT

Semantic Role Labeling based on AllenNLP implementation of Shi et al, 2019. Can be trained using both PropBank and VerbAatlas inventories and implements also the predicate disambiguation task, in addition to arguments identification and disambiguation.

To-Dos

• Works with both PropBank and VerbAtlas (infer inventory from dataset reader)
• Compatibility with all models from Huggingface's Transformers. - Now works only with models that accept 1 as token type id
• Predicate identification (without using spacy)

Infos

• Language Model: BERT
• Dataset: CoNLL 2012

Results with VerbAtlas

With bert-base-cased:

# Dev set
- F1 arguments 87.6
- F1 predicates 95.5
# Test set
- F1 arguments x
- F1 predicates x

With bert-base-multilingual-cased:

# Dev set
- F1 arguments 86.2
- F1 predicates 94.2
# Test set
- F1 arguments 86.1
- F1 predicates 94.9