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.

Example Input: We used the following affective resources relying on different emotion models.

Emolex: it contains 14,182 words associated with eight primary emotion based on the Plutchik model BIBREF10 , BIBREF11 .

EmoSenticNet(EmoSN): it is an enriched version of SenticNet BIBREF12 including 13,189 words labeled by six Ekman's basic emotion BIBREF13 , BIBREF14 .

Dictionary of Affect in Language (DAL): includes 8,742 English words labeled by three scores representing three dimensions: Pleasantness, Activation and Imagery BIBREF15 .

Affective Norms for English Words (ANEW): consists of 1,034 English words BIBREF16 rated with ratings based on the Valence-Arousal-Dominance (VAD) model BIBREF17 .

Linguistic Inquiry and Word Count (LIWC): this psycholinguistic resource BIBREF18 includes 4,500 words distributed into 64 emotional categories including positive (PosEMO) and negative (NegEMO). 
 Question: What affective-based features are used?
Example Output: Abstractive

Example Input: In Table TABREF1, we summarize the quantitative results of the above previous studies. It includes the model basis, feature description, languages classified and the used dataset along with accuracy obtained. The table also lists the overall results of our proposed models (at the top). The languages used by various authors along with their acronyms are English (En), Spanish (Es), French (Fr), German (De), Russian (Ru), Italian (It), Bengali (Ben), Hindi (Hi) and Telegu (Tel). 
 Question: Is the performance compared against a baseline model?
Example Output: Yes-no

Example Input: After using Kytea, Japanese texts are applied LSW algorithm to replace OOV words by their synonyms.  
 Question: Are synonymous relation taken into account in the Japanese-Vietnamese task?
Example Output:
Yes-no