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.

We compare our method to the following baselines: (1) Single-task CNN: training a CNN model for each task individually; (2) Single-task FastText: training one FastText model BIBREF23 with fixed embeddings for each individual task; (3) Fine-tuned the holistic MTL-CNN: a standard transfer-learning approach, which trains one MTL-CNN model on all the training tasks offline, then fine-tunes the classifier layer (i.e. $\mathrm {M}^{(cls)}$ Figure 1 (a)) on each target task; (4) Matching Network: a metric-learning based few-shot learning model trained on all training tasks; (5) Prototypical Network: a variation of matching network with different prediction function as Eq. 9 ; (6) Convex combining all single-task models: training one CNN classifier on each meta-training task individually and taking the encoder, then for each target task training a linear combination of all the above single-task encoders with Eq. ( 24 ).  
 Question: Do they compare with the MAML algorithm?
Yes-no

We make copies of the monolingual model for each language and add additional crosslingual latent variables (CLVs) to couple the monolingual models, capturing crosslingual semantic role patterns. Concretely, when training on parallel sentences, whenever the head words of the arguments are aligned, we add a CLV as a parent of the two corresponding role variables.  
 Question: Which additional latent variables are used in the model?
Extractive

To ground moral sentiment in text, we leverage the Moral Foundations Dictionary BIBREF27. The MFD is a psycholinguistic resource that associates each MFT category with a set of seed words, which are words that provide evidence for the corresponding moral category in text. To implement the first tier of our framework and detect moral relevance, we complement our morally relevant seed words with a corresponding set of seed words approximating moral irrelevance based on the notion of valence, i.e., the degree of pleasantness or unpleasantness of a stimulus. We refer to the emotional valence ratings collected by BIBREF28 for approximately 14,000 English words, and choose the words with most neutral valence rating that do not occur in the MFD as our set of morally irrelevant seed words, for an equal total number of morally relevant and morally irrelevant words. We divide historical time into decade-long bins, and use two sets of embeddings provided by BIBREF30, each trained on a different historical corpus of English:

Google N-grams BIBREF31: a corpus of $8.5 \times 10^{11}$ tokens collected from the English literature (Google Books, all-genres) spanning the period 1800-1999.

COHA BIBREF32: a smaller corpus of $4.1 \times 10^8$ tokens from works selected so as to be genre-balanced and representative of American English in the period 1810-2009. 
 Question: Which datasets are used in the paper?

Abstractive