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.

We train a single layer LSTM with a 150-dimensional hidden state for hate / not hate classification. The input dimensionality is set to 100 and GloVe BIBREF26 embeddings are used as word input representations. 
 Question: What unimodal detection models were used?
 single layer LSTM with a 150-dimensional hidden state for hate / not hate classification

ACL-ARC is a dataset of citation intents released by BIBREF7 . The dataset is based on a sample of papers from the ACL Anthology Reference Corpus BIBREF15 and includes 1,941 citation instances from 186 papers and is annotated by domain experts in the NLP field. 
 Question: What is the size of ACL-ARC datasets?
includes 1,941 citation instances from 186 papers

We used a dataset provided by Shortir, an Indonesian news aggregator and summarizer company. The dataset contains roughly 20K news articles. Each article has the title, category, source (e.g., CNN Indonesia, Kumparan), URL to the original article, and an abstractive summary which was created manually by a total of 2 native speakers of Indonesian 
 Question: Did they use a crowdsourcing platform for the summaries?

No