TASK DEFINITION: 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.
PROBLEM: If we re-train the skip-gram model and update the emoji map periodically on new escort ads, when traffickers switch to new emojis, the map can link the new emojis to the old ones, assisting anti-trafficking experts in expanding the lexicon of trafficking flags. This approach also works for acronyms and deliberate typos.

SOLUTION: How is the lexicon of trafficking flags expanded?

PROBLEM: We extract data from the WMT'14 English-French (En-Fr) and English-German (En-De) datasets. To create a larger discrepancy between the tasks, so that there is a clear dataset size imbalance, the En-De data is artificially restricted to only 1 million parallel sentences, while the full En-Fr dataset, comprising almost 40 million parallel sentences, is used entirely.

SOLUTION: What datasets are used for experiments?

PROBLEM: 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).

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