You will be given a definition of a task first, then some input of the task.
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.

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?
Output:
Abstractive