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.

Ex Input:
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?

Ex Output:
No

Ex Input:
To simplify the task, we set up a binary classification: candidates who have been liked or shortlisted are considered part of the hirable class and others part of the not hirable class.
Question: How is "hirability" defined?

Ex Output:
candidates who have been liked or shortlisted are considered part of the hirable class

Ex Input:
We consider two types of architectures: a bi-directional language model to augment the sequence to sequence encoder and a uni-directional model to augment the decoder. Both use self-attention BIBREF16 and the uni-directional model contains INLINEFORM0 transformer blocks, followed by a word classifier to predict the next word on the right. bi-directional language model to augment the sequence to sequence encoder
Question: What language model architectures are used?

Ex Output:
uni-directional model to augment the decoder