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.

Ex Input:
We used two different corpora for the experiments: small_parallel_enja and Asian Scientific Paper Excerpt Corpus (ASPEC) BIBREF5.

Ex Output:
Which dataset do they use?

Ex Input:
We define the STransE score function $\mathcal {R}$1 as follows:

$ f_r(h, t) & = & \Vert extbf {W}_{r,1} extbf {h} + extbf {r} - extbf {W}_{r,2} extbf {t}\Vert _{\ell _{1/2}} $

using either the $\ell _1$ or the $\ell _2$ -norm (the choice is made using validation data; in our experiments we found that the $\ell _1$ norm gave slightly better results).

Ex Output:
What scoring function does the model use to score triples?

Ex Input:
I also performed several case studies. I obtained document embeddings, in the same latent space as the topic embeddings, by summing the posterior mean vectors INLINEFORM0 for each token, and visualized them in two dimensions using INLINEFORM1 -SNE BIBREF24 (all vectors were normalized to unit length). The state of the Union addresses (Figure FIGREF27 ) are embedded almost linearly by year, with a major jump around the New Deal (1930s), and are well separated by party at any given time period.

Ex Output:
What is an example of a computational social science NLP task?