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.

Input: Consider Input: We make copies of the monolingual model for each language and add additional crosslingual latent variables (CLVs) to couple the monolingual models, capturing crosslingual semantic role patterns. Concretely, when training on parallel sentences, whenever the head words of the arguments are aligned, we add a CLV as a parent of the two corresponding role variables. 
 Question: Do they add one latent variable for each language pair in their Bayesian model?

Output: Yes


Input: Consider Input: As the baseline, we simply judge the input token as IOCs on the basis of the spelling features described in BIBREF12 . 
 Question: What is used a baseline?

Output: As the baseline, we simply judge the input token as IOCs on the basis of the spelling features described in BIBREF12


Input: Consider Input: As we mention in the Section 2, CAEVO is the current state-of-the-art system for feature-based temporal event relation extraction BIBREF10 . It's widely used as the baseline for evaluating TB-Dense data. We adopt it as our baseline for evaluating CaTeRS and RED datasets. 
 Question: What type of baseline are established for the two datasets?


Output: CAEVO