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.

[EX Q]: We chose SVMhmm BIBREF111 implementation of Structural Support Vector Machines for sequence labeling. SVM implementation of
Question: Which machine learning methods are used in experiments?
[EX A]: Structural Support Vector Machine

[EX Q]: 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?
[EX A]: fragments are interchangeable if they occur in at least one lexical environment that is exactly the same

[EX Q]: This decoding approach closely follows Algorithm SECREF7 , but along with soft back pointers, we also compute hard back pointers at each time step. After computing all the relevant quantities like model score, loss etc., we follow the hard backpointers to obtain the best sequence INLINEFORM0 .
Question: Do they compare partially complete sequences (created during steps of beam search) to gold/target sequences?
[EX A]:
Yes