instruction:
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
question:
Figure FIGREF23 shows the proposed ontology, which, in our evaluation procedure, was populated with 3121 events entries from 51 documents.
Question: How is the effectiveness of this pipeline approach evaluated?
answer:
proposed ontology, which, in our evaluation procedure, was populated with 3121 events entries from 51 documents.

question:
The SnapCaptions dataset is composed of 10K user-generated image (snap) and textual caption pairs where named entities in captions are manually labeled by expert human annotators (entity types: PER, LOC, ORG, MISC).
Question: How large is their MNER SnapCaptions dataset?
answer:
10K user-generated image (snap) and textual caption pairs

question:
Besides, we report the sentiment accuracy (Senti ACC) which measures whether the output sentence has the same sentiment polarity as the input sentence based on our standardized sentiment classifiers. The BLEU score BIBREF25 between the input sentences and the output sentences is calculated to evaluate the content preservation performance. In order to evaluate the overall performance of different models, we also report the geometric mean (G2) and harmonic mean (H2) of the sentiment accuracy and the BLEU score. As for the irony accuracy, we only report it in human evaluation results because it is more accurate for the human to evaluate the quality of irony as it is very complicated.
Question: Who judged the irony accuracy, sentiment preservation and content preservation?
answer:
Irony accuracy is judged only by human ; senriment preservation and content preservation are judged both by human and using automatic metrics (ACC and BLEU).