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.

Input: Consider Input: We then show the tweet text and image and we ask them to classify it in one of 6 categories: No attacks to any community, racist, sexist, homophobic, religion based attacks or attacks to other communities.
Question: What annotations are available in the dataset - tweat used hate speach or not?

Output: No attacks to any community racist sexist homophobic religion based attacks attacks to other communities

Input: Consider Input: For example, it can be used to extract features for usage with other machine learning tools, or to evaluate given features with the existing classifiers or regressors. Figure FIGREF19 demonstrates a simple feature extractor that retrieves the sentence length.
Question: Do they show an example of usage for INFODENS?

Output: Yes

Input: Consider Input: First we are comparing BiLSTMBIBREF14, BiLSTM+CNNBIBREF20, BiLSTM+CRFBIBREF1, BiLSTM+CNN+CRFBIBREF2 models with CNN modelBIBREF0 and Stanford CRF modelBIBREF21.
Question: Which models are used to solve NER for Nepali?
Output: BiLSTMBIBREF14, BiLSTM+CNNBIBREF20, BiLSTM+CRFBIBREF1, BiLSTM+CNN+CRFBIBREF2 CNN modelBIBREF0 and Stanford CRF modelBIBREF21