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.

Ex Input:
n this paper we provide a novel real-time and adaptive cryptocurrency price prediction platform based on Twitter sentiments. The integrative and modular platform copes with the three aforementioned challenges in several ways. Firstly, it provides a Spark-based architecture which handles the large volume of incoming data in a persistent and fault tolerant way. Secondly, the proposed platform offers an approach that supports sentiment analysis based on VADER which can respond to large amounts of natural language processing queries in real time. Thirdly, the platform supports a predictive approach based on online learning in which a machine learning model adapts its weights to cope with new prices and sentiments. Finally, the platform is modular and integrative in the sense that it combines these different solutions to provide novel real-time tool support for bitcoin price prediction that is more scalable, data-rich, and proactive, and can help accelerate decision-making, uncover new opportunities and provide more timely insights based on the available and ever-larger financial data volume and variety.
Question: Which elements of the platform are modular?

Ex Output:
Abstractive

Ex Input:
Human Judgments
Following BIBREF11 , BIBREF12 and the vast amount of previous work on semantic similarity, we ask nine undergraduate subjects to assess the similarity of 360 pairs of relations from a subset of Wikidata BIBREF8 that are chosen to cover from high to low levels of similarity. In our experiment, subjects were asked to rate an integer similarity score from 0 (no similarity) to 4 (perfectly the same) for each pair.
Question: How do they gather human judgements for similarity between relations?

Ex Output:
Abstractive

Ex Input:
The input of our model are the words in the input text $x[1], ... , x[n]$ and query $q[1], ... , q[n]$ . We concatenate pre-trained word embeddings from GloVe BIBREF40 and character embeddings trained by CharCNN BIBREF41 to represent input words. The $2d$ -dimension embedding vectors of input text $x_1, ... , x_n$ and query $q_1, ... , q_n$ are then fed into a Highway Layer BIBREF42 to improve the capability of word embeddings and character embeddings as

$$egin{split} g_t &= {
m sigmoid}(W_gx_t+b_g) \ s_t &= {
m relu } (W_xx_t+b_x) \ u_t &= g_t \odot s_t + (1 - g_t) \odot x_t~. \end{split}$$ (Eq. 18) The same Highway Layer is applied to $q_t$ and produces $v_t$ . Next, $u_t$ and $v_t$ are fed into a Bi-Directional Long Short-Term Memory Network (BiLSTM) BIBREF44 respectively in order to model the temporal interactions between sequence words: Then we feed $\mathbf {U}$ and $\mathbf {V}$ into the attention flow layer BIBREF27 to model the interactions between the input text and query. Therefore, we introduce Self-Matching Layer BIBREF29 in our model as

$$egin{split} o_t &= {
m BiLSTM}(o_{t-1}, [h_t, c_t]) \ s_j^t &= w^T {
m tanh}(W_hh_j+ ilde{W_h}h_t)\ lpha _i^t &= {
m exp}(s_i^t)/\Sigma _{j=1}^n{
m exp}(s_j^t)\ c_t &= \Sigma _{i=1}^nlpha _i^th_i ~. \end{split}$$ (Eq. 20) Finally we feed the embeddings $\mathbf {O} = [o_1, ... , o_n]$ into a Pointer Network BIBREF39 to decode the answer sequence as

$$egin{split} p_t &= {
m LSTM}(p_{t-1}, c_t) \ s_j^t &= w^T {
m tanh}(W_oo_j+W_pp_{t-1})\ eta _i^t &= {
m exp}(s_i^t)/\Sigma _{j=1}^n{
m exp}(s_j^t)\ c_t &= \Sigma _{i=1}^neta _i^to_i~. \end{split}$$ (Eq. 21) Therefore, the probability of generating the answer sequence $ extbf {a}$ is as follows

$${
m P}( extbf {a}|\mathbf {O}) = \prod _t {
m P}(a^t | a^1, ... , a^{t-1}, \mathbf {O})~.$$ (Eq. 23)
Question: What QA models were used?

Ex Output:
Abstractive