instruction:
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.
question:
Following these methods, we evaluate NCEL on the following five datasets: (1) CoNLL-YAGO BIBREF22 : the CoNLL 2003 shared task including testa of 4791 mentions in 216 documents, and testb of 4485 mentions in 213 documents. (2) TAC2010 BIBREF39 : constructed for the Text Analysis Conference that comprises 676 mentions in 352 documents for testing. (3) ACE2004 BIBREF23 : a subset of ACE2004 co-reference documents including 248 mentions in 35 documents, which is annotated by Amazon Mechanical Turk. (4) AQUAINT BIBREF40 : 50 news articles including 699 mentions from three different news agencies. (5) WW BIBREF19 : a new benchmark with balanced prior distributions of mentions, leading to a hard case of disambiguation. It has 6374 mentions in 310 documents automatically extracted from Wikipedia. 
 Question: Which datasets do they use?
answer:
Extractive


question:
Even if LangID-High does not present a more accurate result, it does present a more compact one: LangID-High is 15.4 MB, while the combined wFST high resource models are 197.5 MB. 
 Question: how is model compactness measured?
answer:
Abstractive


question:
On the same architecture, our RCRN outperforms ablative baselines BiLSTM by INLINEFORM2 and 3L-BiLSTM by INLINEFORM3 on average across 16 datasets. 
 Question: By how much do they outperform BiLSTMs in Sentiment Analysis?
answer:

Abstractive