verbalization about ask2transformers HOT 4 CLOSED

Hi @SapaePhyu !

The eassiest way to reproduce the results is by running the following command:

python -m a2t.evaluation --config resources/predefined_configs/{DATASET}.arguments.config.json

Where {DATASET} can be ace or wikievents.

However, answering your question, the templates contain placeholders for different information, such as the event trigger ({trg}), trigger type ({trg_type}) and of course, the filler candidate ({arg}). So, on your example:

text = "..."
template = "{arg} bought something."
verbalization = template.format(**{"arg": "John D. Idol", "trg": "hired", "trg_type": "..."})

model_input = "{original_sentence} </s> {verbalization}".format(original_sentence=text, verbalization=verbalization)

Note that this is a very simplified example.

You can see the templates on the appendix of the paper or in the task configs at resources/predefined_configs/.

from ask2transformers.

Thank you, Sainz! Is it correct to think that the inputs of placeholders are the outputs of NER? If so, is there any constraint of selecting the entities, or do the placeholders accept all entities one by one?

from ask2transformers.

Yes! The actual models perform trigger-entity classification in order to assign role fillers to the events. Depending on your evaluation you would want to use gold entities or some entities predicted from a NER model. Regarding the constraints, in the configuration file for each dataset there are defined what we call valid_conditions, that is, the trigger type entity type pairs that are allowed for a given role. Although the probabilities are computed for every trigger-entity pair, only the ones that fulfill the valid conditions are accepted as valid answers.

from ask2transformers.

Thank you so much for your detailed explanation!!

from ask2transformers.