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: The only remaining question is what makes two environments similar enough to infer the existence of a common category. There is, again, a large literature on this question (including the aforementioned language modeling, unsupervised parsing, and alignment work), but in the current work we will make use of a very simple criterion: fragments are interchangeable if they occur in at least one lexical environment that is exactly the same. 
 Question: How do they determine similar environments for fragments in their data augmentation scheme?

Answer: fragments are interchangeable if they occur in at least one lexical environment that is exactly the same


Question: For English dataset, we crawl 687 cybersecurity articles from a collection of advanced persistent threats (APT) reports which are published from 2008 to 2018.  
 Question: Where are the cybersecurity articles used in the model sourced from?

Answer:  from a collection of advanced persistent threats (APT) reports which are published from 2008 to 2018


Question: In this paper, we analyze the task of automatically correcting run-on sentences. We develop two methods: a conditional random field model (roCRF) and a Seq2Seq attention model (roS2S) and show that they outperform models from the sister tasks of punctuation restoration and whole-sentence grammatical error correction. 
 Question: Which machine learning models do they use to correct run-on sentences?

Answer:
conditional random field model Seq2Seq attention model