instruction:
In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.
question:
We apply the Generative Pre-trained Transformer (GPT) BIBREF2 as our pre-trained language model.  
 Question: What pretrained LM is used?
answer:
Generative Pre-trained Transformer (GPT)


question:
We evaluate our model on both English and Chinese segmentation. For both languages we used standard datasets for word segmentation and language modeling. English
The Brent corpus is a standard corpus used in statistical modeling of child language acquisition BIBREF15 , BIBREF16  We use the commonly used version of the PTB prepared by BIBREF17 Since Chinese orthography does not mark spaces between words, there have been a number of efforts to annotate word boundaries. We evaluate against two corpora that have been manually segmented according different segmentation standards. The Beijing University Corpus was one of the corpora used for the International Chinese Word Segmentation Bakeoff BIBREF18 . We use the Penn Chinese Treebank Version 5.1 BIBREF19 . 
 Question: What dataset is used?
answer:
Brent corpus PTB  Beijing University Corpus Penn Chinese Treebank


question:
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. 
 Question: How is the lexicon of trafficking flags expanded?
answer:

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