TASK DEFINITION: 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.
PROBLEM: This section introduces our probabilistic model that infers keyword expectation and trains the target model simultaneously. 
 Question: What type of classifiers are used?

SOLUTION: probabilistic model

PROBLEM: he immediate impact of the explicit constraint is avoiding the collapse issue ($D_{KL}=0$) by setting a non-zero positive constraint ($C\ge 0$) on the KL term ($|D_{KL}\big (q_\phi ({z}|{x}) || p({z})\big )-C|$). 
 Question: Why does proposed term help to avoid posterior collapse?

SOLUTION: by setting a non-zero positive constraint ($C\ge 0$) on the KL term ($|D_{KL}\big (q_\phi ({z}|{x}) || p({z})\big )-C|$)

PROBLEM: We provide a hand-curated collection of complete inflection tables for 198 lemmata. The morphological tags follow the guidelines of the UniMorph schema BIBREF6, BIBREF7, in order to allow for the potential of cross-lingual transfer learning, and they are tagged with respect to:

Person: first (1), second (2), and third (3)

Number: singular (SG) ad plural (PL)

Inclusivity (only applicable to first person plural verbs: inclusive (INCL) and exclusive (EXCL)

Aspect/mood: completive (CPL), progressive (PROG), potential (POT), and habitual (HAB). 
 Question: How was annotation done?

SOLUTION:
 hand-curated collection of complete inflection tables for 198 lemmata