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.

Q: We use the Bayesian model of garg2012unsupervised as our base monolingual model. The semantic roles are predicate-specific. 
 Question: What does an individual model consist of?

A: Bayesian model of garg2012unsupervised as our base monolingual model
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Q: We experiment and compare with the following models.

Pointer-Gen is the baseline model trained by optimizing $L_\text{MLE}$ in Equation DISPLAY_FORM13.

Pointer-Gen+Pos is the baseline model by training Pointer-Gen only on positive examples whose sensationalism score is larger than 0.5

Pointer-Gen+Same-FT is the model which fine-tunes Pointer-Gen on the training samples whose sensationalism score is larger than 0.1

Pointer-Gen+Pos-FT is the model which fine-tunes Pointer-Gen on the training samples whose sensationalism score is larger than 0.5

Pointer-Gen+RL-ROUGE is the baseline model trained by optimizing $L_\text{RL-ROUGE}$ in Equation DISPLAY_FORM17, with ROUGE-L BIBREF9 as the reward.

Pointer-Gen+RL-SEN is the baseline model trained by optimizing $L_\text{RL-SEN}$ in Equation DISPLAY_FORM17, with $\alpha _\text{sen}$ as the reward. 
 Question: Which baselines are used for evaluation?

A: Pointer-Gen Pointer-Gen+Pos Pointer-Gen+Same-FT Pointer-Gen+Pos-FT Pointer-Gen+RL-ROUGE Pointer-Gen+RL-SEN
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Q: For a purpose of learning from limited annotated linguistic resources, our preliminary discovery shows that it is possible to build a geometric space projection between embedding spaces to help cross-lingual NE recognition. 
 Question: What is their model?

A:
cross-lingual NE recognition
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