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.
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Question: To counter that, we use a left-to-right attention mask, similar to the one employed in the original Transformer decoder BIBREF1. For the input tokens in $X$, we apply such mask to all the target tokens $Y$ that were concatenated to $X$, so that input tokens can only attend to the other input tokens. Conversely, for target tokens $y_t$, we put an attention mask on all tokens $y_{>t}$, allowing target tokens $y_t$ to attend only to the input tokens and the already generated target tokens. 
 Question: What is different in BERT-gen from standard BERT?

Answer: Abstractive


Question: We asked for two distinct paraphrases of each sentence because we believe that a good sentence embedding should put paraphrases close together in vector space.

Several modification types were specifically selected to constitute a thorough test of embeddings. In different meaning, the annotators should create a sentence with some other meaning using the same words as the original sentence. Other transformations which should be difficult for embeddings include minimal change, in which the sentence meaning should be significantly changed by using only very small modification, or nonsense, in which words of the source sentence should be shuffled so that it is grammatically correct, but without any sense. 
 Question: What annotations are available in the dataset?

Answer: Abstractive


Question: Our model consists of two neural network modules, i.e. an extractor and abstractor. The extractor encodes a source document and chooses sentences from the document, and then the abstractor paraphrases the summary candidates.  The extractor is based on the encoder-decoder framework. We adapt BERT for the encoder to exploit contextualized representations from pre-trained transformers. We use LSTM Pointer Network BIBREF22 as the decoder to select the extracted sentences based on the above sentence representations.  Our abstractor is practically identical to the one proposed in BIBREF8. 
 Question: What's the method used here?

Answer:
Abstractive