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.

[Q]: Inspired by the psycholinguistic studies BIBREF5 , BIBREF4 , BIBREF7 proposed a semi-character based RNN (ScRNN) that processes a sentence of words with misspelled characters, predicting the correct words at each step. Let $s = \lbrace w_1, w_2, \dots , w_n\rbrace $ denote the input sentence, a sequence of constituent words $w_i$ . Each input word ( $w_i$ ) is represented by concatenating (i) a one hot vector of the first character ( $\mathbf {w_{i1}}$ ); (ii) a one hot representation of the last character ( $\mathbf {w_{il}}$ , where $l$ is the length of word $w_i$ ); and (iii) a bag of characters representation of the internal characters ( $\sum _{j=2}^{l-1}\mathbf {w_{ij}})$ . ScRNN treats the first and the last characters individually, and is agnostic to the ordering of the internal characters. Each word, represented accordingly, is then fed into a BiLSTM cell. At each sequence step, the training target is the correct corresponding word (output dimension equal to vocabulary size), and the model is optimized with cross-entropy loss. 
 Question: What is a semicharacter architecture?
[A]: Abstractive


[Q]: It is worth mentioning that the collected texts contain a large quantity of errors of several types: orthographic, syntactic, code-switched words (i.e. words not in the required language), jokes, etc. Hence, the original written sentences have been processed in order to produce “cleaner” versions, in order to make the data usable for some research purposes (e.g. to train language models, to extract features for proficiency assessment, ...). 
 Question: Are any of the utterances ungrammatical?
[A]: Yes-no


[Q]: Our ASR and ST models follow the architecture in berard2018end, but have 3 decoder layers like that in pino2019harnessing. 
 Question: What is the architecture of their model?
[A]:
Extractive