In this task, you will be presented with a context from an academic paper and a question based on the context. You have to classify the questions into "Extractive", "Abstractive", or "Yes-no" questions. Extractive questions can be answered by concatenating extracts taken from a context into a summary while answering abstractive questions involves paraphrasing the context using novel sentences. Yes-no question is a question whose expected answer is one of two choices, one that affirms the question and one that denies the question. Typically, the choices are either yes or no.

With BERT, two fully unsupervised tasks are performed. The Masked Language Model and the Next Sentence Prediction (NSP).

For this study, the NSP is used as a proxy for the relevance of response.
Question: was bert used?
Yes-no

To better exploit such existing data sources, we propose an end-to-end (E2E) model based on pointer networks with attention, which can be trained end-to-end on the input/output pairs of human IE tasks, without requiring token-level annotations. Since our model does not need token-level labels, we create an E2E version of each data set without token-level labels by chunking the BIO-labeled words and using the labels as fields to extract.
Question: Do they assume sentence-level supervision?
Yes-no

We have training and testing sets in three different languages: English, Chinese and Korean. When fine-tuning, we simply adopted the official training script of BERT, with default hyperparameters, to fine-tune each model until training loss converged.
Question: what does the model learn in zero-shot setting?
Extractive