TASK DEFINITION: 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.
PROBLEM: However, all previous IE benchmarks BIBREF18 are too small to train neural network models typically used in QA, and thus we need to build a large benchmark. Therefore, we build a large scale benchmark named QA4IE benchmark which consists of 293K Wikipedia articles and 2M golden relation triples with 636 different relation types. We manually find 148 relations which can be projected to a WikiData relation out of 2064 DBpedia relations. 
 Question: Was this benchmark automatically created from an existing dataset?

SOLUTION: Yes-no

PROBLEM: We use word alignments, similarly to other annotation projection work, to project the AMR alignments to the target languages. Our approach depends on an underlying assumption that we make: if a source word is word-aligned to a target word and it is AMR aligned with an AMR node, then the target word is also aligned to that AMR node. Word alignments were generated using fast_align BIBREF10 , while AMR alignments were generated with JAMR BIBREF11 . 
 Question: How is annotation projection done when languages have different word order?

SOLUTION: Abstractive

PROBLEM: While manually analyzing the raw dataset, we noticed that looking at the tweet one has replied to or has quoted, provides significant contextual information. We call these, “context tweets". As humans can better understand a tweet with the reference of its context, our assumption is that computers also benefit from taking context tweets into account in detecting abusive language.

As shown in the examples below, (2) is labeled abusive due to the use of vulgar language. However, the intention of the user can be better understood with its context tweet (1).

(1) I hate when I'm sitting in front of the bus and somebody with a wheelchair get on.

INLINEFORM0 (2) I hate it when I'm trying to board a bus and there's already an as**ole on it.

Similarly, context tweet (3) is important in understanding the abusive tweet (4), especially in identifying the target of the malice.

(3) Survivors of #Syria Gas Attack Recount `a Cruel Scene'.

INLINEFORM0 (4) Who the HELL is “LIKE" ING this post? Sick people....

Huang et al. huang2016modeling used several attributes of context tweets for sentiment analysis in order to improve the baseline LSTM model. However, their approach was limited because the meta-information they focused on—author information, conversation type, use of the same hashtags or emojis—are all highly dependent on data.

In order to avoid data dependency, text sequences of context tweets are directly used as an additional feature of neural network models. We use the same baseline model to convert context tweets to vectors, then concatenate these vectors with outputs of their corresponding labeled tweets. More specifically, we concatenate max-pooled layers of context and labeled tweets for the CNN baseline model. As for RNN, the last hidden states of context and labeled tweets are concatenated. 
 Question: What additional features and context are proposed?

SOLUTION:
Abstractive