In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
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Question: We extract data from the WMT'14 English-French (En-Fr) and English-German (En-De) datasets. To create a larger discrepancy between the tasks, so that there is a clear dataset size imbalance, the En-De data is artificially restricted to only 1 million parallel sentences, while the full En-Fr dataset, comprising almost 40 million parallel sentences, is used entirely. 

Answer: What datasets are used for experiments?


Question: Our analysis found that gang members have a penchant for using just a small set of emoji symbols that convey their anger and violent behavior through their tweets. Figure FIGREF24 illustrates the emoji distribution for the top 20 most frequent emojis used by gang member profiles in our dataset. The fuel pump emoji was the most frequently used emoji by the gang members, which is often used in the context of selling or consuming marijuana. The pistol emoji is the second most frequent in our dataset, which is often used with the guardsman emoji or the police cop emoji in an `emoji chain'. Figure FIGREF28 presents some prototypical `chaining' of emojis used by gang members. The chains may reflect their anger at law enforcement officers, as a cop emoji is often followed up with the emoji of a weapon, bomb, or explosion. We found that 32.25% of gang members in our dataset have chained together the police and the pistol emoji, compared to just 1.14% of non-gang members. Moreover, only 1.71% of non-gang members have used the hundred points emoji and pistol emoji together in tweets while 53% of gang members have used them. A variety of the angry face emoji such as devil face emoji and imp emoji were also common in gang member tweets.

Answer: What are the differences in the use of emojis between gang member and the rest of the Twitter population?


Question: Benchmark Evaluation ::: Classifier Models
SVM: A linear support vector machine with bag-of-words sentence representations.

MLP: A multi-layer perceptron with USE embeddings BIBREF4 as input.

FastText: A shallow neural network that averages embeddings of n-grams BIBREF5.

CNN: A convolutional neural network with non-static word embeddings initialized with GloVe BIBREF6.

BERT: A neural network that is trained to predict elided words in text and then fine-tuned on our data BIBREF1.

Platforms: Several platforms exist for the development of task-oriented agents. We consider Google's DialogFlow and Rasa NLU with spacy-sklearn.

Answer:
Which classifiers are evaluated?