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: Following the previous works, we report the precision ($P.$), recall ($R.$) and $F_1$ scores for target recognition and targeted sentiment. 

Answer: How is the effectiveness of the model evaluated?


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

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

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


Question: Each one of these tokenizers, we combine with some types of representation methods, including word to vector methods such as continuous bag of words BIBREF5, pre-trained embedding as fasttext (trained on Wiki Vietnamese language) BIBREF6 and sonvx (trained on Vietnamese newspaper) BIBREF7 The dataset in this HSD task is really imbalance. Clean class dominates with 91.5%, offensive class takes 5% and the rest belongs to hate class with 3.5%.

Answer:
What dataset do they use?